A Remark on Concept Drift for Dependent Data
Hinder F, Vaquet V, Hammer B (2024)
In: Advances in Intelligent Data Analysis XXII. 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, April 24–26, 2024, Proceedings, Part I. Miliou I, Piatkowski N, Papapetrou P (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 77-89.
Grimmelsmann N, Mechtenberg M, Vieth M, Schulz A, Hammer B, Schneider A (2024)
In: Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications: 611-621.
Semantic Properties of Cosine Based Bias Scores for Word Embeddings
Schroeder S, Schulz A, Hinder F, Hammer B (2024)
In: Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods. Vol. 1. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications: 160-168.
Physics-Informed Graph Neural Networks for Water Distribution Systems
Ashraf MI, Strotherm J, Hermes L, Hammer B (2024)
Presented at the 38th Annual AAAI Conference on Artificial Intelligence 2024, Vancouver.
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles
Muschalik M, Fumagalli F, Hammer B, Hüllermeier E (2024)
Proceedings of the AAAI Conference on Artificial Intelligence 38(13): 14388-14396.
Kenneweg T, Mueller S, Brixner T, Pfeiffer W (2024)
Computer Physics Communications 296: 109031.
Innovations in Deep Learning for Biotechnology
Stallmann D (2024)
Bielefeld: Universität Bielefeld.
Contrasting Explanations in Machine Learning. Efficiency, Robustness & Applications
Artelt A (2024)
Bielefeld: Universität Bielefeld.
Dealing with Inaccurate and Incomplete Labels in Industrial Streaming Data
Castellani A (2024)
Bielefeld: Universität Bielefeld.
SHAP-IQ: Unified Approximation of any-order Shapley Interactions
Fumagalli F, Muschalik M, Kolpaczki P, Hüllermeier E, Hammer B (2023)
In: Advances in Neural Information Processing Systems 36 (NeurIPS 2023).
Nelkner J, Huang L, Lin TW, Schulz A, Osterholz B, Henke C, Blom J, Pühler A, Sczyrba A, Schlüter A (2023)
Environmental Microbiome 18(1): 26.
Towards Detecting Lexical Change of Hate Speech in Historical Data
Hoeken S, Spliethoff S, Schwandt S, Zarrieß S, Alaçam Ö (2023)
In: Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change. Tahmasebi N (Ed); Stroudsburg, PA, USA: Association for Computational Linguistics: 100-111.
Kuhl U, Artelt A, Hammer B (2023)
Frontiers in Computer Science 5: 1087929.
Model-based explanations of concept drift
Hinder F, Vaquet V, Brinkrolf J, Hammer B (2023)
Neurocomputing: 126640.
Learning Vector Quantization for the Real-World: Privacy, Robustness, and Sparsity
Brinkrolf J (2023)
Bielefeld: Universität Bielefeld.
Generating Cardiovascular Data to Improve Training of Assistive Heart Devices
Kummert J, Schulz A, Feldhans R, Habigt M, Stemmler M, Kohler C, Abel D, Rossaint R, Hammer B (2023)
In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE: 1292-1297.
Data Augmentation for Cardiovascular Time Series Data Using WaveNet
Feldhans R, Schulz A, Kummert J, Habigt M, Stemmler M, Kohler C, Abel D, Rossaint R, Hammer B (2023)
In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE: 836-841.
Unsupervised Unlearning of Concept Drift with Autoencoders
Artelt A, Malialis K, Panayiotou CG, Polycarpou MM, Hammer B (2023)
In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE: 703-710.
Robust Feature Selection and Robust Training to Cope with Hyperspectral Sensor Shifts
Vaquet V, Brinkrolf J, Hammer B (Accepted) .
Feature Selection for Concept Drift Detection
Hinder F, Hammer B (2023)
In: ESANN 2023 Proceedings. Verleysen M (Ed); .
On Feature Removal for Explainability in Dynamic Environments
Fumagalli F, Muschalik M, Hüllermeier E, Hammer B (2023)
In: ESANN 2023 proceedings. 83-88.
iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams
Muschalik M, Fumagalli F, Hammer B, Hüllermeier E (2023)
In: Machine Learning and Knowledge Discovery in Databases: Research Track. European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III. Koutra D, Plant C, Gomez Rodriguez M, Baralis E, Bonchi F (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 428-445.
Incremental permutation feature importance (iPFI): towards online explanations on data streams
Fumagalli F, Muschalik M, Hüllermeier E, Hammer B (2023)
Machine Learning .
iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios
Muschalik M, Fumagalli F, Jagtani R, Hammer B, Hüllermeier E (2023)
In: Explainable Artificial Intelligence. First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part I. Longo L (Ed); Communications in Computer and Information Science. Cham: Springer Nature Switzerland: 177-194.
Behavioral Economics and Neuroeconomics of Environmental Values
Koundouri P, Hammer B, Kuhl U, Velias A (2023)
Annual Review of Resource Economics 15(1): 153-176.
Best of both, Structured and Unstructured Sparsity in Neural Networks
Schulte-Schüren C, Wagner S, Runge A, Bariamis D, Hammer B, Yoneki E, Nardi L (2023)
In: Proceedings of the 3rd Workshop on Machine Learning and Systems. New York, NY, USA: ACM: 104-108.
Faster Convergence for Transformer Fine-tuning with Line Search Methods
Kenneweg P, Galli L, Kenneweg T, Hammer B (2023)
In: 2023 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-8.
Kuhl U, Artelt A, Hammer B (2023)
In: Explainable Artificial Intelligence. First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part III. Longo L (Ed); Communications in Computer and Information Science. Cham: Springer Nature Switzerland: 280-300.
"I do not know! but why?"- Local model-agnostic example-based explanations of reject
Artelt A, Visser R, Hammer B (2023)
Neurocomputing 558: 126722.
Fairness-Enhancing Ensemble Classification in Water Distribution Networks
Strotherm J, Hammer B (2023)
Presented at the International Work-Conference on Artificial Neural Networks (IWANN) 2023, Ponta Delgada.
Strotherm J, Müller A, Hammer B, Paaßen B (2023) .
Schroeder S, Schulz A, Tarakanov I, Feldhans R, Hammer B (2023)
In: Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I. Rojas I, Joya G, Catala A (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 134-145.
One-Class Intrusion Detection with Dynamic Graphs
Liuliakov A, Schulz A, Hermes L, Hammer B (2023)
In: Artificial Neural Networks and Machine Learning – ICANN 2023. 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26–29, 2023, Proceedings, Part IV. Iliadis L, Papaleonidas A, Angelov P, Jayne C (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 537-549.
Adversarial Attacks on Leakage Detectors in Water Distribution Networks
Stahlhofen P, Artelt A, Hermes L, Hammer B (2023)
In: Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part II. Rojas I, Joya G, Catala A (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 451-463.
Extending Drift Detection Methods to Identify When Exactly the Change Happened
Vieth M, Schulz A, Hammer B (2023)
In: Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I. Rojas I, Joya G, Catala A (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 92-104.
On the Hardness and Necessity of Supervised Concept Drift Detection
Hinder F, Vaquet V, Brinkrolf J, Hammer B (2023)
In: Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM. Vol. 1. De Marsico M, Sanniti di Baja G, Fred A (Eds); Setúbal: SCITEPRESS - Science and Technology Publications: 164-175.
LU-Net: Invertible Neural Networks Based on Matrix Factorization
Chan RK-W, Penquitt S, Gottschalk H (2023)
In: 2023 International Joint Conference on Neural Networks (IJCNN). Piscataway, NJ: IEEE: 1-10.
Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis
Stallmann D, Hammer B (2023)
Algorithms 16(4): 205.
AutoML technologies for the identification of sparse classification and outlier detection models
Liuliakov A, Hermes L, Hammer B (2023)
Applied Soft Computing 133: 109942.
Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams
Jakob J, Artelt A, Hasenjäger M, Hammer B (2023)
Applied Artificial Intelligence 37(1): 2198846.
Metropolitan Segment Traffic Speeds From Massive Floating Car Data in 10 Cities
Neun M, Eichenberger C, Xin Y, Fu C, Wiedemann N, Martin H, Tomko M, Ambühl L, Hermes L, Kopp M (2023)
IEEE Transactions on Intelligent Transportation Systems .
What should AI see? Using the public’s opinion to determine the perception of an AI
Chan RK-W, Dardashti R, Osinski M, Rottmann M, Brüggemann D, Rücker C, Schlicht P, Hüger F, Rummel N, Gottschalk H (2023)
AI and Ethics.
Learning with high dimensional data and preprocessing in non-stationary environments
Heusinger M (2023)
Bielefeld: Universität Bielefeld.
Metric Learning with Self-Adjusting Memory for Explaining Feature Drift
Kummert J, Schulz A, Hammer B (2023)
SN Computer Science 4(4): 376.
"Why Here and not There?": Diverse Contrasting Explanations of Dimensionality Reduction
Artelt A, Schulz A, Hammer B (2023)
In: Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications: 27-38.
Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects
Maag K, Chan RK-W, Uhlemeyer S, Kowol K, Gottschalk H (2023)
In: Computer Vision – ACCV 2022. 16th Asian Conference on Computer Vision, Macao, China, December 4–8, 2022, Proceedings, Part V. Wang L, Gall J, Chin T-J, Sato I, Chellappa R (Eds); Lecture Notes in Computer Science, 13845. Cham: Springer Nature Switzerland: 476-494.
Modularity in Nervous Systems-a Key to Efficient Adaptivity for Deep Reinforcement Learning
Schilling M, Hammer B, Ohl FW, Ritter H, Wiskott L (2023)
Cognitive Computation.
Guiding Information: Supervised Models and their Relationship with Data
Göpfert C (2023)
Bielefeld: Universität Bielefeld.
Long-Term Transitioning of Water Distribution Systems: ERC Water-Futures Project
Savic D, Hammer B, Koundouri P, Polycarpou M (2022)
In: Proceedings - 2nd International Join Conference on Water Distribution System Analysis (WDSA)& Computing and Control in the Water Industry (CCWI). València: Editorial Universitat Politècnica de València.
Novel transfer learning schemes based on Siamese networks and synthetic data
Kenneweg P, Stallmann D, Hammer B (2022)
Neural Computing and Applications 35: 8423–8436.
Contrasting Explanations for Understanding and Regularizing Model Adaptations
Artelt A, Hinder F, Vaquet V, Feldhans R, Hammer B (2022)
Neural Processing Letters 55: 5273–5297.
Artelt A, Geminn C, Hammer B, Manzeschke A, Mavrina L, Weber C (2022)
DuEPublico: Duisburg-Essen Publications online, University of Duisburg-Essen, Germany.
Agnostic Explanation of Model Change based on Feature Importance
Muschalik M, Fumagalli F, Hammer B, Hüllermeier E (2022)
KI - Künstliche Intelligenz.
Suitability of Different Metric Choices for Concept Drift Detection
Hinder F, Vaquet V, Hammer B (2022)
In: Advances in Intelligent Data Analysis XX. 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20–22, 2022, Proceedings. Bouadi T, Fromont E, Hüllermeier E (Eds); Lecture Notes in Computer Science. Cham: Springer International Publishing: 157-170.
Reject Options for Incremental Regression Scenarios
Jakob J, Hasenjäger M, Hammer B (2022)
In: Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV. Pimenidis E, Angelov P, Jayne C, Papaleonidas A, Aydin M (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 248-259.
Localization of Concept Drift: Identifying the Drifting Datapoints
Hinder F, Vaquet V, Brinkrolf J, Artelt A, Hammer B (2022)
In: 2022 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-9.
SAM-kNN Regressor for Online Learning in Water Distribution Networks
Jakob J, Artelt A, Hasenjäger M, Hammer B (2022)
In: Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part III. Pimenidis E, Angelov P, Jayne C, Papaleonidas A, Aydin M (Eds); Lecture Notes in Computer Science, 13531. Cham: Springer Nature : 752-762.
Sparse Factor Autoencoders for Item Response Theory
Paaßen B, Dywel M, Fleckenstein M, Pinkwart N (2022)
In: Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022). Cristea AI, Brown C, Mitrovic T, Bosch N (Eds); 17–26.
“Even if …” – Diverse Semifactual Explanations of Reject
Artelt A, Hammer B (2022)
In: 2022 IEEE Symposium Series on Computational Intelligence (SSCI). Ishibuchi H (Ed); Piscataway, NJ: IEEE: 854-859.
Explaining Reject Options of Learning Vector Quantization Classifiers
Artelt A, Brinkrolf J, Visser R, Hammer B (2022)
In: Proceedings of the 14th International Joint Conference on Computational Intelligence. SCITEPRESS - Science and Technology Publications: 249-261.
Recursive Tree Grammar Autoencoders
Paaßen B, Koprinska I, Yacef K (2022)
Machine Learning 111: 3393–3423.
A conceptual graph-based model of creativity in learning
Paaßen B, Dehne J, Krishnaraja S, Kovalkov A, Gal K, Pinkwart N (2022)
Frontiers in Education 7.
Few-shot Keypose Detection for Learning of Psychomotor Skills
Paaßen B, Baumgartner T, Geisen M, Riedl N, Kravčík M (2022)
In: Proceedings of the Second International Workshop on Multimodal Immersive Learning Systems ({MILeS} 2022). Asyraaf Mat Sanusi K, Limbu B, Schneider J, Di Mitri D, Klemke R (Eds); 22–27.
Interpretable Knowledge Gain Prediction for Vocational Preparatory E-Learnings
Paaßen B, Dywel M, Fleckenstein M, Pinkwart N (2022)
In: Proceedings of the 23rd International Conference on Artificial Intelligence in Education (AIED 2022) Practitioner’s Track. DeFalco JA, Matos DDM da C, Blanc B, Reichow I (Eds); 132–137.
Faster Confidence Intervals for Item Response Theory via an Approximate Likelihood
Paaßen B, Göpfert C, Pinkwart N (2022)
In: Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022). Cristea AI, Brown C, Mitrovic T, Bosch N (Eds); 555–559.
Picones G, Paaßen B, Koprinska I, Yacef K (2022)
In: Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022). Cristea AI, Brown C, Mitrovic T, Bosch N (Eds); 217–227.
Reservoir Memory Machines as Neural Computers
Paaßen B, Schulz A, C. Stewart T, Hammer B (2022)
IEEE Transactions on Neural Networks and Learning Systems 33(6): 2575–2585.
Kuhl U, Artelt A, Hammer B (2022)
In: 2022 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM: 2125-2137.
Federated learning vector quantization for dealing with drift between nodes
Vaquet V, Hinder F, Brinkrolf J, Menz P, Seiffert U, Hammer B (Accepted)
Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges.
Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data
Vaquet V, Menz P, Seiffert U, Hammer B (2022)
Neurocomputing.
Localization of Concept Drift: Identifying the Drifting Datapoints
Hinder F, Vaquet V, Brinkrolf J, Artelt A, Hammer B (2022) .
One Explanation to Rule them All — Ensemble Consistent Explanations
Artelt A, Vrachimis S, Eliades D, Polycarpou M, Hammer B (2022)
ArXiv:2205.08974 .
Taking care of our drinking water: Dealing with Sensor Faults in Water Distribution Networks
Vaquet V, Artelt A, Brinkrolf J, Hammer B (2022)
Presented at the 31st International Conference on Artificial Neural Networks, Bristol.
Neural Architecture Search for Sentence Classification with BERT
Kenneweg P, Schroeder S, Hammer B (2022)
In: ESANN 2022 proceedings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 417-422.
Detecting Anything Overlooked in Semantic Segmentation
Chan RK-W (2022)
Bergische Universität Wuppertal.
Detecting and Learning the Unknown in Semantic Segmentation
Chan RK-W, Uhlemeyer S, Rottmann M, Gottschalk H (2022)
In: Deep Neural Networks and Data for Automated Driving. Robustness, Uncertainty Quantification, and Insights Towards Safety. Fingscheidt T, Gottschalk H, Houben S (Eds); Cham: Springer International Publishing: 277-313.
Robustness in Machine Learning: Adversarial Perturbations, Explanations & Intuition
Göpfert JP (2022)
Bielefeld: Universität Bielefeld.
Explainable Artificial Intelligence for Improved Modeling of Processes
Velioglu R, Göpfert JP, Artelt A, Hammer B (2022)
In: Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Yin H, Camacho D, Tino P (Eds); Lecture Notes in Computer Science, 13756. Cham: Springer International Publishing: 313-325.
Efficient Sensor Selection for Individualized Prediction Based on Biosignals
Vieth M, Grimmelsmann N, Schneider A, Hammer B (2022)
In: Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Yin H, Camacho D, Tino P (Eds); Lecture Notes in Computer Science, 13756. Cham: Springer International Publishing: 326-337.
Intelligent Learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers
Kenneweg P, Schulz A, Schroeder S, Hammer B (2022)
In: Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Yin H, Camacho D, Tino P (Eds); Lecture Notes in Computer Science, 13756. Cham: Springer International Publishing: 252-261.
Learning in non-stationary Environments
Raab C (2022)
Bielefeld: Universität Bielefeld.
Interpretable locally adaptive nearest neighbors
Göpfert JP, Wersing H, Hammer B (2022)
Neurocomputing 470: 344-351.
A comparative study of neural machine translation models for Turkish language
Ozdemir O, Akin ES, Velioglu R, Dalyan T (2022)
Journal of Intelligent & Fuzzy Systems 42(3): 2103-2113.
Relevance learning for redundant features
Pfannschmidt L (2021)
Bielefeld: Universität Bielefeld.
Mapping Python Programs to Vectors using Recursive Neural Encodings
Paaßen B, McBroom J, Jeffries B, Koprinska I, Yacef K (2021)
Journal of Educational Datamining 13(3): 1–35.
AutoML Technologies for the Identification of Sparse Models
Liuliakov A, Hammer B (2021)
In: Intelligent Data Engineering and Automated Learning – IDEAL 2021. 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings. Yin H, Camacho D, Tino P, Allmendinger R, Tallón-Ballesteros AJ, Tang K, Cho S-B, Novais P, Nascimento S (Eds); Lecture Notes in Computer Science, 13113. Cham: Springer : 65-75.
On the suitability of incremental learning for regression tasks in exoskeleton control
Jakob J, Hasenjäger M, Hammer B (2021)
In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE: 1-8.
Task-Sensitive Concept Drift Detector with Constraint Embedding
Castellani A, Schmitt S, Hammer B (2021)
In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE: 01-08.
Castellani A, Schmitt S, Hammer B (2021)
In: Machine Learning and Knowledge Discovery in Databases. Research Track. European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part I. Oliver N, Pérez-Cruz F, Kramer S, Read J, Lozano JA (Eds); Lecture Notes in Computer Science, 12975. Cham: Springer International Publishing: 469-484.
Fast Non-Parametric Conditional Density Estimation using Moment Trees
Hinder F, Vaquet V, Brinkrolf J, Hammer B (2021)
IEEE Computational Intelligence Magazine.
Convex optimization for actionable & plausible counterfactual explanations
Artelt A, Hammer B (2021)
arXiv: 2105.07630v1.
Automatic Creativity Measurement in Scratch Programs Across Modalities
Kovalkov A, Paaßen B, Segal A, Pinkwart N, Gal K (2021)
IEEE Transactions on Learning Technologies 14(6): 740–753.
Bacciu D, Bianchi FM, Paaßen B, Alippi C (2021)
In: {Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021)}. Verleysen M (Ed); 89–98.
Teaching psychomotor skills using machine learning for error detection
Paaßen B, Kravčík M (2021)
In: Proceedings of the 1st International Workshop on Multimodal Immersive Learning Systems ({MILeS} 2021). Klemke R, Asyraaf Mat Sanusi K (Eds); 8–14.
Paaßen B, Grattarola D, Zambon D, Alippi C, Hammer B (2021)
In: Proceedings of the Ninth International Conference on Learning Representations (ICLR 2021). Mohamed S, Hofmann K, Oh A, Murray N, Titov I (Eds); .
An A*-algorithm for the Unordered Tree Edit Distance with Custom Costs
Paaßen B (2021)
In: Proceedings of the 14th International Conference on Similarity Search and Applications (SISAP 2021). Reyes N, Connor R, Kriege N, Kazempour D, Bartolini I, Schubert E, Chen J-J (Eds); Springer: 364–371.
Modeling Creativity in Visual Programming: From Theory to Practice
Kovalkov A, Paaßen B, Segal A, Gal K, Pinkwart N (2021)
In: Proceedings of the 15th {International Conference on Educational Data Mining} ({EDM} 2021). Bouchet F, Vie J-J, Hsiao S, Sahebi S (Eds); International Educational Datamining Society.
Analyzing Student Success and Mistakes in Virtual Microscope Structure Search Tasks
Paaßen B, Bertsch A, Langer-Fischer K, Rüdian S, Wang X, Sinha R, Kuzilek J, Britsch S, Pinkwart N (2021)
In: Proceedings of the 15th {International Conference on Educational Data Mining} ({EDM} 2021). Bouchet F, Vie J-J, Hsiao S, Sahebi S (Eds); International Educational Datamining Society.
Progress Networks as a Tool for Analysing Student Programming Difficulties
McBroom J, Paaßen B, Jeffries B, Koprinska I, Yacef K (2021)
In: Proceedings of the Twenty-Third Australasian Computing Education Conference (ACE '21). Szabo C, Sheard J (Eds); Association for Computing Machinery: 158–167.
Online Learning on Non-Stationary Data Streams for Image Recognition using Deep Embeddings
Vaquet V, Hinder F, Vaquet J, Brinkrolf J, Hammer B (2021)
IEEE Symposium Series on Computational Intelligence: 1-7.
A Shape-Based Method for Concept Drift Detection and Signal Denoising
Hinder F, Brinkrolf J, Vaquet V, Hammer B (2021)
In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE: 01-08.
Fast Non-Parametric Conditional Density Estimation using Moment Trees
Hinder F, Vaquet V, Brinkrolf J, Hammer B (2021)
In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE: 1-7.
Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data
Vaquet V, Menz P, Seiffert U, Hammer B (2021)
In: ESANN 2021 proceedings. Verleysen M (Ed); Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 47-52.
Szczuka J, Artelt A, Geminn C, Hammer B, Kopp S, Manzeschke A, Rossnagel A, Slawik P, Strathmann C, Szymczyk N, Varonina L, et al. (2021)
Essen: Universität Duisburg-Essen, Universitätsbibliothek.
Artelt A, Hammer B (2021)
Neurocomputing 470(VSI: ESANN 2020): 304-317.
Evaluating Robustness of Counterfactual Explanations
Artelt A, Vaquet V, Velioglu R, Hinder F, Brinkrolf J, Schilling M, Hammer B (2021)
In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE: 01-09.
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
Chan RK-W, Lis K, Uhlemeyer S, Blum H, Honari S, Siegwart R, Fua P, Salzmann M, Rottmann M (2021)
In: Proceedings of the Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks.
Software architecture for human- centered reliability assessment for neural networks in autonomous
Brüggemann D, Chan RK-W, Gottschalk H, Bracke S (2021)
In: Proc. of the 11th IMA International Conference on Modelling in Industrial Maintenance and Reliability. Institute of Mathematics & its Applications.
Chan RK-W, Rottmann M, Gottschalk H (2021)
In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE: 5108-5117.
How to Compare Adversarial Robustness of Classifiers from a Global Perspective
Risse N, Göpfert C, Göpfert JP (2021)
In: Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I. Farkaš I, Masulli P, Otte S, Wermter S (Eds); Lecture Notes in Computer Science, 12891. Cham: Springer International Publishing: 29-41.
Kuhl U, Sobotta S, Skeide MA (2021)
PLOS Biology 19(9): e3001407.
Paaßen B, Schulz A, Hammer B (2021)
Neurocomputing 470: 352-364.
Intuitiveness in Active Teaching
Göpfert JP, Kuhl U, Hindemith L, Wersing H, Hammer B (2021)
IEEE Transactions on Human-Machine Systems: 1-10.
Reservoir Memory Machines as Neural Computers
Paassen B, Schulz A, Stewart TC, Hammer B (2021)
IEEE Transactions on Neural Networks and Learning Systems: 1-11.
Towards an automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation
Stallmann D, Göpfert JP, Schmitz J, Grünberger A, Hammer B (Accepted)
Bioinformatics .
Interpretable analysis of motion data
Hosseini B (2021)
Bielefeld: Universität Bielefeld.
Concept Drift Segmentation via Kolmogorov Trees
Hinder F, Hammer B (Accepted)
In: Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); .
Federated Learning Vector Quantization
Brinkrolf J, Hammer B (Accepted)
In: Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); .
Efficient Reject Options for Particle Filter Object Tracking in Medical Applications
Kummert J, Schulz A, Redick T, Ayoub N, Modabber A, Abel D, Hammer B (2021)
Sensors 21(6): 2114.
Supervised learning in the presence of concept drift: a modelling framework
Straat M, Abadi F, Kan Z, Göpfert C, Hammer B, Biehl M (2021)
Neural Computing and Applications.
Resting-State Functional Connectivity in Mathematical Expertise
Shim M, Hwang H-J, Kuhl U, Jeon H-A (2021)
Brain Sciences 11(4): 430.
Schulz A, Hinder F, Hammer B (2020)
In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}.
Balanced SAM-kNN: Online Learning with Heterogeneous Drift and Imbalanced Data
Vaquet V, Hammer B (2020)
In: Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II. Farkaš I, Masulli P, Wermter S (Eds); Lecture Notes in Computer Science, 12397. Cham: Springer: 850-862.
Tree Echo State Autoencoders with Grammars
Paaßen B, Koprinska I, Yacef K (2020)
In: Proceedings of the 2020 International Joint Conference on Neural Networks ({IJCNN} 2020). Roy A (Ed); 1–8.
Improving and Evaluating Conversational User Interfaces for Children
Krämer N, Szczuka J, Rossnagel A, Geminn C, Kopp S, Hammer B, Mavrina L, Artelt A, Manzeschke A, Weber C (2020)
In: IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces. New York: Association for Computing Machinery.
Kinder als Nutzende smarter Sprachassistenten Spezieller Gestaltungsbedarf zum Schutz von Kindern
Geminn CL, Szczuka J, Weber C, Artelt A, Varonina L (2020)
Datenschutz und Datensicherheit - DuD 44(9): 600-605.
Hinder F, Artelt A, Hammer B (2020)
In: Proceedings of the 37th International Conference on Machine Learning.
Efficient computation of counterfactual explanations of LVQ models
Artelt A, Hammer B (2020)
In: ESANN 2020 - proceedings. Verleysen M (Ed); Louvain-la-Neuve: Ciaco : 19-24.
Convex Density Constraints for Computing Plausible Counterfactual Explanations
Artelt A, Hammer B (2020)
In: Artificial Neural Networks and Machine Learning - ICANN 2020 - 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part {I}. Farkas I, Masulli P, Wermter S (Eds); Lecture Notes in Computer Science, 12396. Cham: Springer: 353-365.
Detection of False Positive and False Negative Samples in Semantic Segmentation
Rottmann M, Maag K, Chan RK-W, Huger F, Schlicht P, Gottschalk H (2020)
In: 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE: 1351-1356.
Rottmann M, Colling P, Paul Hack T, Chan RK-W, Huger F, Schlicht P, Gottschalk H (2020)
In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-9.
Controlled False Negative Reduction of Minority Classes in Semantic Segmentation
Chan RK-W, Rottmann M, Huger F, Schlicht P, Gottschalk H (2020)
In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-8.
Chan RK-W, Rottmann M, Gottschalk H, Hüger F, Schlicht P (2020)
In: Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference. Baraldi P, Maio FD, Zio E (Eds); Singapore: Research Publishing Services: 3065-3072.
Velioglu R, Rose J (2020)
In: arXiv:2012.12975.
Sparse Metric Learning in Prototype-based Classification
Brinkrolf J, Hammer B (2020)
In: Proceedings of the ESANN, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); 375-380.
Paaßen B, Schulz A (2020)
In: Proceedings of the 28th European Symposium on Artificial Neural Networks (ESANN 2020). Verleysen M (Ed); Bruges: i6doc: 567-572.
Prototype-Based Online Learning on Homogeneously Labeled Streaming Data
Limberg C, Göpfert JP, Wersing H, Ritter H (2020)
In: Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II. Farkaš I, Masulli P, Wermter S (Eds); Lecture Notes in Computer Science, 12397. Cham: Springer International Publishing: 204-213.
New Prototype Concepts in Classification Learning
Saralajew S (2020)
Bielefeld: Universität Bielefeld.
Pfannschmidt L, Jakob J, Hinder F, Biehl M, Tino P, Hammer B (2020)
Neurocomputing.
Panda A, Yadav A, Yeerna H, Singh A, Biehl M, Lux M, Schulz A, Klecha T, Doniach S, Khiabanian H, Ganesan S, et al. (2020)
Nucleic acids research.
Stallmann D (2020)
Bielefeld University.
The Gendered Nature and Malleability of Gamer Stereotypes
Morgenroth T, Stratemeyer M, Paaßen B (2020)
Cyberpsychology, Behavior, and Social Networking 23(8): 557-561.
Brain-inspired computing and machine learning
Iliadis LS, Kurkova V, Hammer B (2020)
NEURAL COMPUTING & APPLICATIONS.
Sequential Feature Classification in the Context of Redundancies
Pfannschmidt L, Hammer B (Draft) .
Personalized Online Learning of Whole-Body Motion Classes using Multiple Inertial Measurement Units
Losing V, Yoshikawa T, Hasenjaeger M, Hammer B, Wersing H (2019)
In: 2019 International Conference on Robotics and Automation (ICRA). IEEE: 9530-9536.
On the computation of counterfactual explanations - A survey
Artelt A, Hammer B (2019)
arXiv: 1911.07749v1.
CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox
Artelt A (2019)
Bielefeld University.
The Ethical Dilemma When (Not) Setting up Cost-Based Decision Rules in Semantic Segmentation
Chan RK-W, Rottmann M, Dardashti R, Huger F, Schlicht P, Gottschalk H (2019)
In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE: 1395-1403.
Continuous online user authentication based on keystroke dynamics
Artelt A, Jakob J, Vaquet V (2019)
Presented at the Interdisciplinary College (IK), Günne/Möhnesee, Germany.
Lecture Notes on Applied Optimization
Paaßen B, Artelt A, Hammer B (2019)
Faculty of Technology, Bielefeld University.
When can unlabeled data improve the learning rate?
Göpfert C, Ben-David S, Bousquet O, Gelly S, Tolstikhin I, Urner R (2019)
In: Conference on Learning Theory (COLT).
Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning
Prahm C, Schulz A, Paaßen B, Schoisswohl J, Kaniusas E, Dorffner G, Hammer B, Aszmann O (2019)
IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(5): 956-962.
Feature Relevance Bounds for Ordinal Regression
Pfannschmidt L, Jakob J, Biehl M, Tino P, Hammer B (2019)
In: Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Verleysen M (Ed); Louvain-la-Neuve: i6doc.
Paaßen B (2019)
Bielefeld University.
Kuhl U (2019)
Leipzig: Universität Leipzig.
Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation
Hosseini B, Hammer B (Accepted)
Presented at the 2019 IEEE International Conference on Data Mining (ICDM), Beijing.
Hosseini B, Hammer B (Accepted)
Presented at the The 28th ACM International Conference on Information and Knowledge Management (CIKM) , Beijing.
Adversarial Edit Attacks for Tree Data
Paaßen B (2019)
In: Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2019). Yin H, Camacho D, Tino P (Eds); Lecture Notes in Computer Science, 11871. Cham: Springer: 359-366.
Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold
Hosseini B, Hammer B (2019)
Presented at the 2019 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML), Würzburg.
A Comparison of the Quality of Data-Driven Programming Hint Generation Algorithms
Price TW, Dong Y, Zhi R, Paaßen B, Lytle N, Cateté V, Barnes T (2019)
International Journal of Artificial Intelligence in Education 29(3): 368-395.
FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration
Pfannschmidt L, Göpfert C, Neumann U, Heider D, Hammer B (2019)
Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.
Memory Models for Incremental Learning Architectures
Losing V (2019)
Bielefeld: Universität Bielefeld.
Introduction to Machine Learning - Supplementary notes
Artelt A (2019) .
Dynamic fairness - Breaking vicious cycles in automatic decision making
Paaßen B, Bunge A, Hainke C, Sindelar L, Vogelsang M (2019)
In: Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Verleysen M (Ed); Louvain-la-Neuve: i6doc: 477-482.
Embeddings and Representation Learning for Structured Data
Paaßen B, Gallicchio C, Micheli A, Sperduti A (2019)
In: Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Verleysen M (Ed); 85-94.
Metric Learning for Structured Data
Paaßen B (2019)
Bielefeld: Universität Bielefeld.
Differential privacy for learning vector quantization
Brinkrolf J, Göpfert C, Hammer B (2019)
Neurocomputing 342: 125-136.
Hosseini B, Hammer B (2019)
In: Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Verleysen M (Ed); .
Hosseini B, Hammer B (2019)
Presented at the The 2019 International Joint Conference on Neural Networks (IJCNN), Budapest.
Time integration and reject options for probabilistic output of pairwise LVQ
Brinkrolf J, Hammer B (2019)
Neural Computing and Applications.
Enhancing Very Fast Decision Trees with Local Split-Time Predictions
Losing V, Wersing H, Hammer B (2018)
In: 2018 IEEE International Conference on Data Mining (ICDM). IEEE: 287-296.
Confident Kernel Sparse Coding and Dictionary Learning
Hosseini B, Hammer B (2018)
In: 2018 IEEE International Conference on Data Mining (ICDM). IEEE: 1031-1036.
Sentiment Analysis Using Learning Approaches Over Emojis for Turkish Tweets
Velioglu R, Yildiz T, Yildirim S (2018)
In: 2018 3rd International Conference on Computer Science and Engineering (UBMK). IEEE: 303-307.
Queißer J, Ishihara H, Hammer B, Steil JJ, Asada M (2018)
Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo .
Hülsmann F, Göpfert JP, Hammer B, Kopp S, Botsch M (2018)
Bielefeld University.
Hülsmann F, Göpfert JP, Hammer B, Kopp S, Botsch M (2018)
Computers & Graphics 76: 47-59.
Transfer Learning of Complex Motor Skills on the Humanoid Robot Affetto
Schulz A, Queißer J, Ishihara H, Asada M (2018)
Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo (In Press).
Bootstrapping of parameterized skills through hybrid optimization in task and policy spaces
Queißer J, Steil JJ (2018)
Frontiers in Robotics and AI 5: 49.
Statistical Mechanics of On-Line Learning Under Concept Drift
Straat M, Abadi F, Göpfert C, Hammer B, Biehl M (2018)
ENTROPY 20(10): 775.
Lux M, Brinkman RR, Chauve C, Laing A, Lorenc A, Abeler-Dörner L, Hammer B (2018)
Bioinformatics 34(13): 2245-2253.
Inferring Temporal Structure from Predictability in Bumblebee Learning Flight
Meyer S, Bertrand O, Egelhaaf M, Hammer B (2018)
In: Intelligent Data Engineering and Automated Learning – IDEAL 2018. Yin H, Camacho D, Novais P, Tallón-Ballesteros AJ (Eds); Lecture Notes in Computer Science, 11314. Cham: Springer International Publishing: 508-519.
Differential private relevance learning
Brinkrolf J, Berger K, Hammer B (2018)
In: Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). Verleysen M (Ed); 555-560.
Efficient Grouping Methods for the Annotation and Sorting of Single Cells
Lux M (2018)
Bielefeld: Universität Bielefeld.
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces
Paaßen B, Göpfert C, Hammer B (2018)
Neural Processing Letters 48(2): 669-689.
Non-Negative Local Sparse Coding for Subspace Clustering
Hosseini B, Hammer B (2018)
Advances in Intelligent Data Analysis XVII. IDA 2018.
Confident Kernel Sparse Coding and Dictionary Learning
Hosseini B, Hammer B (In Press)
In: 2018 IEEE International Conference on Data Mining (ICDM).
Feasibility Based Large Margin Nearest Neighbor Metric Learning
Hosseini B, Hammer B (2018)
In: ESANN 2018. Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 219-224.
Expectation maximization transfer learning and its application for bionic hand prostheses
Paaßen B, Schulz A, Hahne J, Hammer B (2018)
Neurocomputing 298: 122-133.
Multi-modal Skill Memories for Online Learning of Interactive Robot Movement Generation
Queißer J (2018)
Bielefeld: Universität Bielefeld.
Paaßen B, Ahmaro A (2018)
Bielefeld University.
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Paaßen B (2018)
Bielefeld University.
Median Generalized Learning Vector Quantization for Distance Data
Paaßen B (2018)
Bielefeld University.
Paaßen B (2018)
Bielefeld University.
Mitigating Concept Drift via Rejection
Göpfert JP, Hammer B, Wersing H (2018)
In: Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I. Kurkova V, Manolopoulos Y, Hammer B, Iliadis L, Maglogiannis I (Eds); Lecture Notes in Computer Science, 11139. Cham: Springer.
Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM)
Losing V, Hammer B, Wersing H (2018)
KNOWLEDGE AND INFORMATION SYSTEMS 54(1): 171-201.
Interpretation of Linear Classifiers by Means of Feature Relevance Bounds
Göpfert C, Pfannschmidt L, Göpfert JP, Hammer B (2018)
Neurocomputing 298: 69-79.
The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces
Paaßen B, Hammer B, Price T, Barnes T, Gross S, Pinkwart N (2018)
Journal of Educational Data Mining 10(1): 1-35.
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Paaßen B, Gallicchio C, Micheli A, Hammer B (2018)
In: Proceedings of the 35th International Conference on Machine Learning (ICML 2018). Dy J, Krause A (Eds); Proceedings of Machine Learning Research, 80. 3973-3982.
Incremental on-line learning: A review and comparison of state of the art algorithms
Losing V, Hammer B, Wersing H (2018)
Neurocomputing 275: 1261-1274.
Interpretable Machine Learning with Reject Option
Brinkrolf J, Hammer B (2018)
at - Automatisierungstechnik 66(4): 283-290.
Linear Supervised Transfer Learning for the Large Margin Nearest Neighbor Classifier
Berger K, Schulz A, Paaßen B, Hammer B (2018)
Presented at the SSCI CIDM 2017.
Revisiting the tree edit distance and its backtracing: A tutorial
Paaßen B (Draft)
arXiv:1805.06869.
Personalized maneuver prediction at intersections
Losing V, Hammer B, Wersing H (2017)
In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE: 1-6.
Imitation learning for a continuum trunk robot
Malekzadeh M, Queißer J, Steil JJ (2017)
In: Proceedings of the 25. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN 2017. Verleysen M (Ed); Louvain-la-Neuve: Ciaco.
Time Series Prediction for Relational and Kernel Data
Paaßen B (2017)
Bielefeld University.
Non-negative Kernel Sparse Coding Frameworks for Efficient Analysis of Motion Data
Hosseini B, Hammer B (2017)
Presented at the BMVA Symposium on Human Activity Recognition and Monitoring, London.
Linear Supervised Transfer Learning Toolbox
Paaßen B, Schulz A (2017)
Bielefeld University.
An EM transfer learning algorithm with applications in bionic hand prostheses
Paaßen B, Schulz A, Hahne J, Hammer B (2017)
In: Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Verleysen M (Ed); Bruges: i6doc.com: 129-134.
Two or three things we do (not) know about distances
Paaßen B (2017)
In: Proceedings of the Ninth Mittweida Workshop on Computational Intelligence (MiWoCI 2017). Schleif F-M, Villmann T (Eds); Machine Learning Reports. 32-33.
Paaßen B (2017)
Bielefeld University.
Probabilistic extension and reject options for pairwise LVQ
Brinkrolf J, Hammer B (2017)
In: 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM). Piscataway, NJ: IEEE.
Efficient Kernelization of Discriminative Dimensionality Reduction
Schulz A, Brinkrolf J, Hammer B (2017)
Neurocomputing 268(SI): 34-41.
Prototype based models for the supervised learning of classificaton schemes
Biehl M, Hammer B, Villmann T (2017)
In: Proc. of the IAU Symposium 325 on Astroinformatics, Sorrento/Italy, October 2016. in press.
Differential Privacy for Learning Vector Quantization
Brinkrolf J, Berger K, Hammer B (2017)
In: New Challenges in Neural Computation.
Self-Adjusting Memory: How to Deal with Diverse Drift Types
Losing V, Hammer B, Wersing H (2017)
Presented at the International Joint Conference on Artificial Intelligence (IJCAI) 2017, Melbourne.
Feature Relevance Bounds for Linear Classification
Göpfert C, Pfannschmidt L, Hammer B (2017)
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco - i6doc.com: 187--192.
Personalized Maneuver Prediction at Intersections
Losing V, Hammer B, Wersing H (2017)
Presented at the IEEE Intelligent Transportation Systems Conference 2017, Yokohama.
Task-Driven Sparse Coding for Classification of Motion Data
Hosseini B, Hammer B (2017)
Presented at the Ninth Mittweida Workshop on Computational Intelligence (MiWoCI 2017), Mittweida.
Label-Noise-Tolerant Classification for Streaming Data
Frenay B, Hammer B (2017)
In: IEEE International Joint Conference on Neural Neworks.
Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results
Aswolinskiy W, Hammer B (2017)
In: Proceedings of the Workshop on New Challenges in Neural Computation (NC2). Machine Learning Reports, 03/2017. Bielefeld: Universität Bielefeld, CITEC.
Paaßen B, Morgenroth T, Stratemeyer M (2017)
Sex Roles 76(7-8): 421-435.
Echo State Networks as Novel Approach for Low-Cost Myoelectric Control
Prahm C, Schulz A, Paaßen B, Aszmann O, Hammer B, Dorffner G (2017)
In: Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017). ten Telje A, Popow C, Holmes JH, Sacchi L (Eds); Lecture Notes in Computer Science, 10259. Springer: 338--342.
Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals
Göpfert C, Göpfert JP, Hammer B (2017)
In: Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments.
Online metric learning for an adaptation to confidence drift
Fischer L, Hammer B, Wersing H (2016)
In: 2016 International Joint Conference on Neural Networks (IJCNN). IEEE: 748-755.
Non-Negative Kernel Sparse Coding for the Analysis of Motion Data
Hosseini B, Hülsmann F, Botsch M, Hammer B (2016)
In: Artificial Neural Networks and Machine Learning – ICANN 2016. E.P. Villa A, Masulli P, Javier Pons Rivero A (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer: 506-514.
acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data
Lux M, Krüger J, Rinke C, Maus I, Schlüter A, Woyke T, Sczyrba A, Hammer B (2016)
BMC Bioinformatics 17(1): 543.
Local Reject Option for Deterministic Multi-class SVM
Kummert J, Paaßen B, Jensen J, Göpfert C, Hammer B (2016)
In: Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. E.P. Villa A, Masulli P, Pons Rivero AJ (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer Nature: 251--258.
Adaptive Handling Assistance for Industrial Lightweight Robots in Simulation
Balayn A, Queißer J, Wojtynek M, Wrede S (2016)
In: 2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR).
Adaptive structure metrics for automated feedback provision in intelligent tutoring systems
Paaßen B, Mokbel B, Hammer B (2016)
Neurocomputing 192(SI): 3-13.
Gaussian process prediction for time series of structured data
Paaßen B, Göpfert C, Hammer B (2016)
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco - i6doc.com: 41--46.
Paaßen B (2016)
Bielefeld University.
Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming
Paaßen B, Jensen J, Hammer B (2016)
In: Proceedings of the 9th International Conference on Educational Data Mining. Barnes T, Chi M, Feng M (Eds); Raleigh, North Carolina, USA: International Educational Datamining Society: 183-190.
Paaßen B (2016)
Bielefeld University.
Dissimilarity-based learning for complex data
Mokbel B (2016)
Bielefeld: Universität Bielefeld.
Learning vector quantization for proximity data
Hofmann D (2016)
Bielefeld: Universität Bielefeld.
Discriminative Dimensionality Reduction in Kernel Space
Schulz A, Hammer B (2016)
In: ESANN2016 Proceedings. 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium,27-29 April 2016. i6doc.com.
Virtual optimisation for improved production planning
Brinkrolf J, Mittag T, Joppen R, Dr\ A, Pietsch K-H, Hammer B (2016)
In: New Challenges in Neural Computation.
Choosing the Best Algorithm for an Incremental On-line Learning Task
Losing V, Hammer B, Wersing H (2016)
Presented at the European Symposium on Artificial Neural Networks, Brügge.
Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning
Göpfert C, Paaßen B, Hammer B (2016)
In: Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. E.P. Villa A, Masulli P, Pons Rivero AJ (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer Nature: 510-517.
Dedicated Memory Models for Continual Learning in the Presence of Concept Drift
Losing V, Hammer B, Wersing H (2016)
Presented at the Continual Learning Workshop of the Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), Barcelona.
Linear Supervised Transfer Learning for Generalized Matrix LVQ
Paaßen B, Schulz A, Hammer B (2016)
In: Proceedings of the Workshop New Challenges in Neural Computation 2016. Hammer B, Martinetz T, Villmann T (Eds); Machine Learning Reports(4). 11-18.
A Probabilistic Model with Adaptive Rejection
Fischer L, Villmann T (2016)
Machine Learning Reports, MLR-01-2016:1-19.
Incremental learning algorithms and applications
Geppert er, Hammer B (2016)
In: ESANN.
Online Metric Learning for an Adaptation to Confidence Drift
Fischer L, Hammer B, Wersing H (2016)
In: Proceedings of International Joint Conference on Neural Networks (IJCNN). Vancouver: IEEE: 748-755.
Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift
Prahm C, Paaßen B, Schulz A, Hammer B, Aszmann O (2016)
In: Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016). Ibáñez J, Gonzáles-Vargas J, Azorín JM, Akay M, Pons JL (Eds); Springer: 153--157.
KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift
Losing V, Hammer B, Wersing H (2016)
In: 2016 IEEE 16th International Conference on Data Mining (ICDM). Piscataway, NJ: IEEE: 291-300.
Prototype-based models in machine learning
Biehl M, Hammer B, Villmann T (2016)
Wiley Interdisciplinary Reviews: Cognitive Science 7(2): 92-111.
Self-Adjusting Reject Options in Prototype Based Classification
Villmann T, Kaden M, Bohnsack A, Villmann JM, Drogies T, Saralajew S, Hammer B (2016)
In: Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016. Merényi E, Mendenhall MJ, O'Driscoll P (Eds); Advances in Intelligent Systems and Computing, 428. Cham: Springer International Publishing: 269-279.
Optimal local rejection for classifiers
Fischer L, Hammer B, Wersing H (2016)
Neurocomputing 214: 445-457.
Rejection and online learning with prototype-based classifiers in adaptive metrical spaces
Fischer L (2016)
Bielefeld: Universität Bielefeld.
Combining offline and online classifiers for life-long learning
Fischer L, Hammer B, Wersing H (2015)
In: 2015 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-8.
Gross S, Mokbel B, Hammer B, Pinkwart N (2015)
KI - Künstliche Intelligenz 29(4): 413-418.
Automated Contamination Detection in Single-Cell Sequencing
Lux M, Hammer B, Sczyrba A (2015)
bioRxiv.
Parametric nonlinear dimensionality reduction using kernel t-SNE
Gisbrecht A, Schulz A, Hammer B (2015)
Neurocomputing 147: 71-82.
Data visualization by nonlinear dimensionality reduction
Gisbrecht A, Hammer B (2015)
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5(2): 51-73.
Sparse conformal prediction for dissimilarity data
Schleif F-M, Zhu X, Hammer B (2015)
Annals of Mathematics and Artificial Intelligence 74(1-2): 95-116.
Efficient Metric Learning for the Analysis of Motion Data
Hosseini B, Hammer B (2015)
In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). Piscataway, NJ: IEEE.
Inferring Feature Relevances From Metric Learning
Schulz A, Mokbel B, Biehl M, Hammer B (2015)
In: 2015 IEEE Symposium Series on Computational Intelligence. Piscataway, NJ: IEEE.
Metric learning for sequences in relational LVQ
Mokbel B, Paaßen B, Schleif F-M, Hammer B (2015)
Neurocomputing 169(SI): 306-322.
Adaptive structure metrics for automated feedback provision in Java programming
Paaßen B, Mokbel B, Hammer B (2015)
In: Proceedings of the ESANN, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); 307-312.
Using Discriminative Dimensionality Reduction to Visualize Classifiers
Schulz A, Gisbrecht A, Hammer B (2015)
Neural Processing Letters 42(1): 27-54.
Visualization of Regression Models Using Discriminative Dimensionality Reduction
Schulz A, Hammer B (2015)
In: Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, 9257. Cham: Springer Science + Business Media: 437-449.
Unsupervised Dimensionality Reduction for Transfer Learning
Blöbaum P, Schulz A, Hammer B (2015)
In: Proceedings. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco: 507-512.
High Dimensional Matrix Relevance Learning
Schleif F-M, Villmann T, Zhu X (2015)
In: 2014 IEEE International Conference on Data Mining Workshop. Piscataway, NJ: IEEE.
Special Issue on Autonomous Learning
Hammer B, Toussaint M (2015)
{KI} 29(4): 323--327.
Discriminative dimensionality reduction for regression problems using the Fisher metric
Schulz A, Hammer B (2015)
In: 2015 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical & Electronics Engineers (IEEE): 1-8.
Adaptive prototype-based dissimilarity learning
Zhu X (2015)
Bielefeld: Universitätsbibliothek Bielefeld.
Metric and non-metric proximity transformations at linear costs
Gisbrecht A, Schleif F-M (2015)
Neurocomputing 167: 643-657.
Optimum Reject Options for Prototype-based Classification
Fischer L, Hammer B, Wersing H (2015) .
Certainty-based Prototype Insertion/Deletion for Classification with Metric Adaptation
Fischer L, Hammer B, Wersing H (2015)
In: ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 7-12.
Combining Offline and Online Classifiers for Life-long Learning
Fischer L, Hammer B, Wersing H (2015)
In: IJCNN, International Joint Conference on Neural Networks. 2808-2815.
Median variants of learning vector quantization for learning of dissimilarity data
Nebel D, Hammer B, Frohberg K, Villmann T (2015)
Neurocomputing 169(SI): 295-305.
A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems
Paaßen B, Mokbel B, Hammer B (2015)
In: Proceedings of the 8th International Conference on Educational Data Mining. Santos OC, Boticario JG, Romero C, Pechenizkiy M, Merceron A, Mitros P, Luna JM, Mihaescu C, Moreno P, Hershkovitz A, Ventura S, et al. (Eds); International Educational Datamining Society: 632-632.
Autonomous Learning of Representations
Walter O, Häb-Umbach R, Mokbel B, Paaßen B, Hammer B (2015)
KI - Künstliche Intelligenz 29(4): 339–351.
Efficient approximations of robust soft learning vector quantization for non-vectorial data
Hofmann D, Gisbrecht A, Hammer B (2015)
Neurocomputing 147: 96-106.
Towards Dimensionality Reduction for Smart Home Sensor Data
Mokbel B, Schulz A (2015)
In: Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015). Hammer B, Martinetz T, Villmann T (Eds); Machine Learning Reports(3). 41-48.
Efficient rejection strategies for prototype-based classification
Fischer L, Hammer B, Wersing H (2015)
Neurocomputing 169(SI): 334-342.
Automatic discovery of metagenomic structure
Lux M, Sczyrba A, Hammer B (2015)
In: 2015 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical & Electronics Engineers (IEEE).
Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming
Paaßen B (2015)
Bielefeld: Bielefeld University.
Metric Learning in Dimensionality Reduction
Schulz A, Hammer B (2015)
In: Proceedings of the International Conference on Pattern Recognition Applications and Methods. Scitepress: 232-239.
Interactive Online Learning for Obstacle Classification on a Mobile Robot
Losing V, Hammer B, Wersing H (2015)
Presented at the International Joint Conference on Neural Networks, Killarney, Ireland.
Stationarity of Matrix Relevance LVQ
Biehl M, Hammer B, Schleif F-M, Schneider P, Villmann T (2015)
In: 2015 International Joint Conference on Neural Networks (IJCNN). IEEE.
Advances in dissimilarity-based data visualisation
Gisbrecht A (2015)
Bielefeld: Universitätsbibliothek Bielefeld.
How to Select an Example? A Comparison of Selection Strategies in Example-Based Learning
Gross S, Mokbel B, Hammer B, Pinkwart N (2014)
In: Intelligent Tutoring Systems. Trausan-Matu S, Boyer KE, Crosby M, Panourgia K (Eds); Lecture Notes in Computer Science. Cham: Springer International Publishing: 340-347.
Valid interpretation of feature relevance for linear data mappings
Frenay B, Hofmann D, Schulz A, Biehl M, Hammer B (2014)
In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). Piscataway, NJ: Institute of Electrical & Electronics Engineers (IEEE): 149-156.
Learning interpretable kernelized prototype-based models
Hofmann D, Schleif F-M, Paaßen B, Hammer B (2014)
Neurocomputing 141: 84-96.
Adaptive Conformal Semi-Supervised Vector Quantization for Dissimilarity Data
Zhu X, Schleif F-M, Hammer B (2014)
Pattern Recognition Letters 49: 138-145.
Learning vector quantization for (dis-)similarities
Hammer B, Hofmann D, Schleif F-M, Zhu X (2014)
NeuroComputing 131: 43-51.
How to Select an Example? A Comparison of Selection Strategies in Example-Based Learning
Gross S, Mokbel B, Hammer B, Pinkwart N (2014)
In: Intelligent Tutoring Systems. Trausan-Matu S, Elizabeth Boyer K, E. Crosby M, Panourgia K (Eds); Lecture Notes in Computer Science, 8474. Springer: 340-347.
Computational Intelligence in Big Data
Jin Y, Hammer B (2014)
IEEE Computational Intelligence Magazine 9(3): 12-13.
Fischer L, Nebel D, Villmann T, Hammer B, Wersing H (2014)
In: Advances in Self-Organizing Maps and Learning Vector Quantization. Villmann T, Schleif F-M, Kaden M, Lange M (Eds); Advances in Intelligent Systems and Computing, 295. Cham: Springer International Publishing: 109-118.
Rejection strategies for learning vector quantization
Fischer L, Hammer B, Wersing H (2014)
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 41-46.
Local Rejection Strategies for Learning Vector Quantization
Fischer L, Hammer B, Wersing H (2014)
In: Artificial Neural Networks and Machine Learning – ICANN 2014. Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa AEP (Eds); Lecture Notes in Computer Science, 8681. Cham: Springer International Publishing: 563-570.
Correlation-based embedding of pairwise score data
Strickert M, Bunte K, Schleif F-M, Huellermeier E (2014)
Neurocomputing 141: 97-109.
Adaptive distance measures for sequential data
Mokbel B, Paaßen B, Hammer B (2014)
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 265-270.
Learning and modeling big data
Hammer B, He H, Martinetz T (2014)
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 343-352.
Example-based feedback provision using structured solution spaces
Gross S, Mokbel B, Paaßen B, Hammer B, Pinkwart N (2014)
International Journal of Learning Technology 9(3): 248-280.
Efficient Adaptation of Structure Metrics in Prototype-Based Classification
Mokbel B, Paaßen B, Hammer B (2014)
In: Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa A (Eds); Lecture Notes in Computer Science, 8681. Springer: 571-578.
Supervised Generative Models for Learning Dissimilarity Data
Nebel D, Hammer B, Villmann T (2014)
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 35-40.
Relevance learning for dimensionality reduction
Schulz A, Gisbrecht A, Hammer B (2014)
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 165-170.
Transfer Learning without given Correspondences
Bloebaum P, Schulz A (2014)
In: Proceedings of the Workshop New Challenges in Neural Computation (NC² 2014). Hammer B, Martinetz T, Villmann T (Eds); Machine Learning Reports. 42-51.
Discriminative Dimensionality Reduction for the Visualization of Classifiers
Gisbrecht A, Schulz A, Hammer B (2014)
In: Pattern Recognition Applications and Methods. Advances in Intelligent Systems and Computing, 318. Cham: Springer Science + Business Media: 39-56.
Generative versus Discriminative Prototype Based Classification
Hammer B, Nebel D, Riedel M, Villmann T (2014)
In: Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, {WSOM} 2014, Mittweida, Germany, July, 2-4, 2014. Cham: Springer International Publishing: 123--132.
A Median Variant of Generalized Learning Vector Quantization
Nebel D, Hammer B, Villmann T (2013)
In: Neural Information Processing. Lee M, Hirose A, Hou Z-G, Kil RM (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 19-26.
Nonlinear dimensionality reduction for cluster identification in metagenomic samples
Gisbrecht A, Hammer B, Mokbel B, Sczyrba A (2013)
In: 17th International Conference on Information Visualisation IV 2013. Banissi E (Ed); Piscataway, NJ: IEEE: 174-179.
Paaßen B (2013)
Bielefeld University.
Applications of discriminative dimensionality reduction
Hammer B, Gisbrecht A, Schulz A (2013)
In: Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods. SCITEPRESS: 33-41.
Domain-Independent Proximity Measures in Intelligent Tutoring Systems
Mokbel B, Gross S, Paaßen B, Pinkwart N, Hammer B (2013)
In: Proceedings of the 6th International Conference on Educational Data Mining (EDM). D'Mello SK, Calvo RA, Olney A (Eds); 334-335.
Regularization and Improved Interpretation of Linear Data Mappings and Adaptive Distance Measures
Strickert M, Hammer B, Villmann T, Biehl M (2013)
In: IEEE SSCI CIDM 2013. IEEE Computational Intelligence Society: 10-17.
Novel approaches in machine learning and computational intelligence
Micheli A, Schleif F-M, Tino P (2013)
Neurocomputing 112: 1-3.
Visualizing the quality of dimensionality reduction
Mokbel B, Lueks W, Gisbrecht A, Hammer B (2013)
Neurocomputing 112: 109-123.
Using Nonlinear Dimensionality Reduction to Visualize Classifiers
Schulz A, Gisbrecht A, Hammer B (2013)
In: Advances in computational intelligence. Proceedings. Vol 1. Rojas I, Joya G, Gabestany J (Eds); Lecture Notes in Computer Science, 7902. Springer: 59-68.
Learning the Appropriate Model Population Structures for Locally Weighted Regression
Vukanovicz S, Schulz A, Haschke R, Ritter H (2013)
In: Workshop New Challenges in Neural Computation 2013. Machine Learning Reports, 2013(02). Bielefeld: Universität Bielefeld: 87.
Towards a Domain-Independent ITS Middleware Architecture
Gross S, Mokbel B, Hammer B, Pinkwart N (2013)
In: 2013 IEEE 13th International Conference on Advanced Learning Technologies. IEEE: 408-409.
Preface: Intelligent interactive data visualization
Hammer B, Keim D, Lawrence N, Lebanon G (2013)
Data Mining and Knowledge Discovery 27(1): 1-3.
Classifier inspection based on different discriminative dimensionality reductions
Schulz A, Gisbrecht A, Hammer B (2013)
In: Workshop NC^2 2013. TR Machine Learning Reports: 77-86.
Visualizing Dependencies of Spectral Features using Mutual Information
Gisbrecht A, Miche Y, Hammer B, Lendasse A (2013)
In: ESANN, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 573-578.
Sparse approximations for kernel learning vector quantization
Hofmann D, Hammer B (2013)
In: ESANN.
Sparse prototype representation by core sets
Schleif F-M, Zhu X, Hammer B (2013)
In: IDEAL 2013. Hujun Yin et.al (Ed); .
Towards Providing Feedback to Students in Absence of Formalized Domain Models
Gross S, Mokbel B, Hammer B, Pinkwart N (2013)
In: AIED. 644-648.
Secure Semi-supervised Vector Quantization for Dissimilarity Data
Zhu X, Schleif F-M, Hammer B (2013)
In: IWANN (1). Rojas I, Joya G, Cabestany J (Eds); Lecture Notes in Computer Science, 7902. Springer: 347-356.
Data Analysis of (Non-)Metric Proximities at Linear Costs
Schleif F-M, Gisbrecht A (2013)
In: Proceedings of SIMBAD 2013. Berlin, Heidelberg: Springer: 59-74.
Semi-Supervised Vector Quantization for proximity data
Zhu X, Schleif F-M, Hammer B (2013)
In: Proceedings of ESANN 2013. 89-94.
A Median Variant of Generalized Learning Vector Quantization
Nebel D, Hammer B, Villmann T (2013)
In: ICONIP (2). 19-26.
Linear Time Relational Prototype Based Learning
Gisbrecht A, Mokbel B, Schleif F-M, Zhu X, Hammer B (2012)
International Journal of Neural Systems 22(05): 1250021.
How to visualize a classifier?
Schulz A, Gisbrecht A, Bunte K, Hammer B (2012)
In: Proceedings of the Workshop - New Challenges in Neural Computation 2012. Machine Learning Reports: 73-83.
Out-of-sample kernel extensions for nonparametric dimensionality reduction
Gisbrecht A, Lueks W, Mokbel B, Hammer B (2012)
In: ESANN 2012. 531-536.
Relevance learning for time series inspection
Gisbrecht A, Sovilj D, Hammer B, Lendasse A (2012)
In: ESANN 2012. Verleysen M (Ed); 489-494.
Special Issue on Neural Learning Paradigms
Hammer B (2012)
Künstliche Intelligenz :KI 26(4): 329-332.
Adaptive Learning for complex-valued data
Bunte K, Schleif F-M, Biehl M (2012)
In: Proceedings of ESANN 2012. 387-392.
How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning?
Mokbel B, Gross S, Lux M, Pinkwart N, Hammer B (2012)
In: Artificial Neural Networks in Pattern Recognition. 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012. Proceedings. Mana N, Schwenker F, Trentin E (Eds); Lecture Notes in Artificial Intelligence, 7477. Springer Berlin Heidelberg: 1-13.
Large margin linear discriminative visualization by Matrix Relevance Learning
Biehl M, Bunte K, Schleif F-M, Schneider P, Villmann T (2012)
In: IJCNN. IEEE Computational Intelligence Society, Institute of Electrical and Electronics Engineers (Eds); Piscataway, NJ: IEEE: 1-8.
Discriminative probabilistic prototype based models in kernel space
Hofmann D, Gisbrecht A, Hammer B (2012)
In: Workshop NC^2 2012. TR Machine Learning Reports.
Efficient Approximations of Kernel Robust Soft LVQ
Hofmann D, Gisbrecht A, Hammer B (2012)
In: WSOM.
Recent developments in clustering algorithms
Bouveyron C, Hammer B, Villmann T (2012)
In: ESANN 2012. Verleysen M (Ed); 447-458.
Linear Basis-Function t-SNE for Fast Nonlinear Dimensionality Reduction
Gisbrecht A, Mokbel B, Hammer B (2012)
In: IJCNN.
How to Visualize Large Data Sets?
Hammer B, Gisbrecht A, Schulz A (2012)
Presented at the Workshop Advances in Self-Organizing Maps (WSOM), Santiago, Chile.
Challenges in Neural Computation
Hammer B (2012)
Künstliche Intelligenz : KI 26(4): 333-340.
Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces
Gross S, Mokbel B, Hammer B, Pinkwart N (2012)
In: DeLFI. 27-38.
Discriminative Dimensionality Reduction Mappings
Gisbrecht A, Hofmann D, Hammer B (2012)
In: Advances in Intelligent Data Analysis XI - 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings. Hollmén J, Klawonn F, Tucker A (Eds); Lecture Notes in Computer Science, 7619. Springer: 126-138.
Kernel Robust Soft Learning Vector Quantization
Hofmann D, Hammer B (2012)
In: Artificial Neural Networks in Pattern Recognition - 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 17-19, 2012. Proceedings. Mana N, Schwenker F, Trentin E (Eds); Lecture Notes in Computer Science, 7477. Springer: 14-23.
Fast approximated relational and kernel clustering
Schleif F-M, Zhu X, Gisbrecht A, Hammer B (2012)
In: Proceedings of ICPR 2012. IEEE: 1229-1232.
Special issue on new challenges in neural computation 2012
Hammer B, Villmann T (2012)
Neurocomputing 131: 1.
Cluster based feedback provision strategies in intelligent tutoring systems
Gross S, Zhu X, Hammer B, Pinkwart N (2012)
In: Proceedings of the 11th international conference on Intelligent Tutoring Systems. Berlin, Heidelberg: Springer-Verlag: 699-700.
Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces
Gross S, Mokbel B, Hammer B, Pinkwart N (2012)
In: DeLFI 2012: Die 10. e-Learning Fachtagung Informatik. Desel J, Haake JM, Spannagel C, Gesellschaft für Informatik (Eds); GI-Edition : Proceedings, 207. Hagen, Germany: Köllen: 27-38.
Soft Competitive Learning for large data sets
Schleif F-M, Zhu X, Hammer B (2012)
In: Proceedings of MCSD 2012. Berlin, Heidelberg: Springer Berlin Heidelberg: 141-151.
Learning Relevant Time Points for Time-Series Data in the Life Sciences
Schleif F-M, Mokbel B, Gisbrecht A, Theunissen L, Dürr V, Hammer B (2012)
In: ICANN (2). Lecture Notes in Computer Science, 7553. Berlin, Heidelberg: Springer Berlin Heidelberg: 531-539.
Limited Rank Matrix Learning, discriminative dimension reduction and visualization
Bunte K, Schneider P, Hammer B, Schleif F-M, Villmann T, Biehl M (2012)
Neural Networks 26: 159-173.
A General Framework for Dimensionality-Reducing Data Visualization Mapping
Bunte K, Biehl M, Hammer B (2012)
Neural Computation 24(3): 771-804.
Visualizing the quality of dimensionality reduction
Mokbel B, Lueks W, Gisbrecht A, Biehl M, Hammer B (2012)
In: ESANN 2012. Verleysen M (Ed); 179--184.
A Conformal Classifier for Dissimilarity Data
Schleif F-M, Zhu X, Hammer B (2012)
In: AIAI (2). Berlin, Heidelberg: Springer Berlin Heidelberg: 234-243.
Patch Processing for Relational Learning Vector Quantization
Zhu X, Schleif F-M, Hammer B (2012)
In: ISNN (1). Berlin, Heidelberg: Springer Berlin Heidelberg: 55-63.
White Box Classification of Dissimilarity Data
Hammer B, Mokbel B, Schleif F-M, Zhu X (2012)
In: HAIS (1). Berlin, Heidelberg: Springer Berlin Heidelberg: 309-321.
Relevance learning for short high-dimensional time series in the life sciences
Schleif F-M, Gisbrecht A, Hammer B (2012)
In: IJCNN. IEEE Computational Intelligence Society, Institute of Electrical and Electronics Engineers (Eds); Piscataway, NJ: IEEE: 1-8.
Approximation techniques for clustering dissimilarity data
Zhu X, Gisbrecht A, Schleif F-M, Hammer B (2012)
Neurocomputing 90: 72-84.
Supervised learning of short and high-dimensional temporal sequences for life science measurements
Schleif F-M, Gisbrecht A, Hammer B (2011) .
Linear time heuristics for topographic mapping of dissimilarity data
Gisbrecht A, Schleif F-M, Zhu X, Hammer B (2011)
In: Intelligent Data Engineering and Automated Learning - IDEAL 2011: IDEAL 2011, 12th international conference, Norwich, UK, September 7 - 9, 2011 ; proceedings. Lecture Notes in Computer Science, 6936. Berlin, Heidelberg: Springer: 25-33.
Topographic Mapping of Dissimilarity Data
Hammer B, Gisbrecht A, Hasenfuss A, Mokbel B, Schleif F-M, Zhu X (2011)
In: WSOM'11.
Accelerating Kernel Neural Gas
Schleif F-M, Gisbrecht A, Hammer B (2011)
In: ICANN'2011. Kaski S, Honkela T, Gitolami M, Dutch W (Eds); .
Generalized Functional Relevance Learning Vector Quantization
Kaestner M, Hammer B, Biehl M, Villmann T (2011)
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D side: pp. 93-98.
A general framework for dimensionality reduction for large data sets
Hammer B, Biehl M, Bunte K, Mokbel B (2011)
In: WSOM'11.
Supervised dimension reduction mappings
Bunte K, Biehl M, Hammer B (2011)
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D side: pp. 281-286.
Relational Generative Topographic Mapping
Gisbrecht A, Mokbel B, Hammer B (2011)
Neurocomputing 74(9): 1359-1371.
Recent Trends in Computational Intelligence in Life Science
Seiffert U, Schleif F-M, Zühlke D (2011)
In: Proceedings of ESANN 2011. 77-86.
Neighbor embedding XOM for dimension reduction and visualization
Bunte K, Hammer B, Villmann T, Biehl M, Wismueller A (2011)
Neurocomputing 74(9): 1340-1350.
Bunte K, Schleif F-M, Villmann T (2011)
In: Proceedings of ESANN 2011. Ciaco - i6doc.com: 29-34.
Advances in artificial neural networks, machine learning, and computational intelligence
Lee JA, Schleif F-M, Martinetz T (2011)
Neurocomputing 74(9): 1299-1300.
Efficient Kernelized Prototype-based Classification
Schleif F-M, Villmann T, Hammer B, Schneider P (2011)
International Journal of Neural Systems 21(06): 443-457.
Accelerating dissimilarity clustering for biomedical data analysis
Gisbrecht A, Hammer B, Schleif F-M, Zhu X (2011)
In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. pp.154-161.
Dimensionality Reduction Mappings
Bunte K, Biehl M, Hammer B (2011)
In: IEEE Symposium on Computational Intelligence and Data Mining. IEEE Computational Intelligence Society (Ed); Piscataway, NJ: IEEE: pp. 349-356.
Sparse Kernel Vector Quantization with Local Dependencies
Schleif F-M (2011)
In: Proceedings of IJCNN 2011. accepted.
Divergence based classification in Learning Vector Quantization
Mwebaze E, Schneider P, Schleif F-M, Aduwo JR, Quinn JA, Haase S, Villmann T, Biehl M (2011)
Neurocomputing 74(9): 1429-1435.
Schleif F-M, Riemer T, Boerner U, Schnapka-Hille L, Cross M (2011)
Bioinformatics 27(4): 524-533.
Zhu X, Hammer B (2011)
Presented at the 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium.
Local matrix adaptation in topographic neural maps
Arnonkijpanich B, Hasenfuss A, Hammer B (2011)
Neurocomputing 74(4): 522-539.
Hierarchical deconvolution of linear mixtures of high-dimensional mass spectra in micro-biology
Schleif F-M, Simmuteit S, Villmann T (2011)
In: Proceedings of AIA 2011. in press.
Multivariate class labeling in Robust Soft LVQ
Schneider P, Geweniger T, Schleif F-M, Biehl M, Villmann T (2011)
In: Proceedings of ESANN 2011. 17-22.
Relevance learning in generative topographic mapping
Gisbrecht A, Hammer B (2011)
Neurocomputing 74(9): 1351-1358.
The Mathematics of Divergence Based Online Learning in Vector Quantization
Villmann T, Haase S, Schleif F-M, Hammer B, Biehl M (2010)
In: Artificial Neural Networks in Pattern Recognition. Schwenker F, El Gayar N (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 108-119.
Abstract Concept Learning Approach Based on Behavioural Feature Extraction
Hosseini B, Ahmadabadi MN, Araabi BN (2010)
In: 2009 Second International Conference on Computer and Electrical Engineering. Kamaruzaman J (Ed); , 2. Piscataway, NJ: IEEE.
The Nystrom approximation for relational generative topographic mappings
Gisbrecht A, Mokbel B, Hammer B (2010)
In: NIPS workshop on challenges of Data Visualization.
Simmuteit S, Schleif F-M, Villmann T (2010)
In: Proceedings of WCSB 2010. 103-106.
Divergence Based Online Learning in Vector Quantization
Villmann T, Haase S, Schleif F-M, Hammer B (2010)
In: Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 6113. Rutkowski L, Scherer R, Tadeusiewicz R, Zadeh L, Zurada J (Eds); Berlin, Heidelberg: Springer: 479-486.
Divergence based Learning Vector Quantization
Mwebaze E, Schneider P, Schleif F-M, Haase S, Villmann T, Biehl M (In Press)
In: Proceedings of ESANN 2010.
Schleif F-M, Riemer T, Boerner U, Schnapka-Hille L, Cross M (2010)
In: Proceedings of WCSB 2010. 91-94.
Local matrix learning in clustering and applications for manifold visualization
Arnonkijpanich B, Hasenfuss A, Hammer B (2010)
Neural Networks 23(4): 476-486.
Hyperparameter learning in probabilistic prototype-based models
Schneider P, Biehl M, Hammer B (2010)
Neurocomputing 73(7-9): 1117-1124.
Global Coordination based on Matrix Neural Gas for Dynamic Texture Synthesis
Arnonkijpanich B, Hammer B (2010)
In: ANNPR'2010. Lecture Notes in Artificial Intelligence, 5998. El Gayar N, Schwenker F (Eds); Springer: 84-95.
Bunte K, Hammer B, Villmann T, Biehl M, Wismüller A (2010)
In: ESANN'10. Proceedings of the 18th European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: D side: 87-92.
Advances in computational intelligence and learning
Angulo C, Lee JA, Schleif F-M (2010)
NeuroComputing 73(7-9): 1049-1050.
Window-Based Example Selection in Learning Vector Quantization
Witoelar AW, Ghosh A, de Vries JJG, Hammer B, Biehl M (2010)
Neural Computing 22(11): 2924-2961.
Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data
Bunte K, Hammer B, Wismueller A, Biehl M (2010)
Neurocomputing 73(7-9): 1074-1092.
Topographic Mapping of Large Dissimilarity Data Sets
Hammer B, Hasenfuss A (2010)
Neural Computation 22(9): 2229-2284.
Regularization in Matrix Relevance Learning
Schneider P, Bunte K, Stiekema H, Hammer B, Villmann T, Biehl M (2010)
IEEE Transactions on Neural Networks 21(5): 831-840.
Generalized derivative based Kernelized learning vector quantization
Schleif F-M, Villmann T, Hammer B, Schneider P, Biehl M (2010)
In: Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Fyfe C, Tino P, Charles D, Garcia-Osorio C, Yin H (Eds); Berlin u.a.: Springer: 21-28.
Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints
Simmuteit S, Schleif F-M, Villmann T, Hammer B (2010)
Knowledge and Information Systems 25(2): 327-343.
Median fuzzy-c-means for clustering dissimilarity data
Geweniger T, Zülke D, Hammer B, Villmann T (2010)
Neurocomputing 73(7-9): 1109-1116.
Perspectives and challenges for recurrent neural network training
Gori M, Hammer B, Hitzler P, Palm G (2010)
Logic Journal of the IGPL 18(5): 617-619.
Clustering very large dissimilarity data sets
Hammer B, Hasenfuss A (2010)
In: Artificial Neural Networks in Pattern Recognition (ANNPR 2010). Proceedings. Schwenker F, El Gayar N (Eds); Lecture Notes in Artificial Intelligence, 5998. Berlin: Springer: 259-273.
Learning paradigms in dynamic environments, 25.07.10-30.07.20
Hammer B, Hitzler P, Maass W, Toussaint M (Eds) (2010) ; 10302.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
On the effect of clustering on quality assessment measures for dimensionality reduction
Mokbel B, Gisbrecht A, Hammer B (2010)
In: NIPS workshop on Challenges of Data Visualization.
Relevance learning in generative topographic maps
Gisbrecht A, Hammer B (2010)
In: ESANN'10. Verleysen M (Ed); Evere: D side: 387-392.
Learning vector quantization for heterogeneous structured data
Zühlke D, Schleif F-M, Geweniger T, Villmann T (2010)
In: Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN) 2010. Evere, Belgium: d-side publications.
The Mathematics of Divergence Based Online Learning in Vector Quanitzation
Villmann T, Haase S, Schleif F-M, Hammer B, Biehl M (2010)
In: ANNPR'2010. El Gayar N, Schwenker F (Eds); Berlin, Heidelberg: Springer: 108-119.
Villmann T, Schleif F-M, Hammer B (2010)
In: ESANN'10. Verleysen M (Ed); D side: 225-234.
Relational Generative Topographic Map
Gisbrecht A, Mokbel B, Hammer B (2010)
In: ESANN'10. Verleysen M (Ed); Evere: D side: 277-282.
Visualizing Dissimilarity Data using generative topographic mapping
Gisbrecht A, Mokbel B, Hasenfuss A, Hammer B (2010)
In: KI'2010. Dillmann R, Beyerer J, Hanebeck UD, Schulz T (Eds); 227-237.
Functional Principal Component Learning Using Oja’s Method and Sobolev Norms
Villmann T, Hammer B (2009)
In: Advances in Self-Organizing Maps. 7th International Workshop, WSOM 2009, St. Augustine, FL, USA, June 8-10, 2009. Proceedings. Príncipe JC, Miikkulainen R (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 325-333.
Some theoretical aspects of the neural gas vector quantizer
Villmann T, Hammer B, Biehl M (2009)
In: Similarity Based Clustering. Biehl M, Hammer B, Verleysen M, Villmann T (Eds); Lecture Notes Artificial Intelligence, 5400. Berlin, Heidelberg: Springer: 23-34.
Equilibrium properties of offline LVQ
Witolaer A, Biehl M, Hammer B (2009)
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); d-side publications: 535-540.
Recent advances in efficient learning of recurrent networks
Hammer B, Schrauwen B, Steil JJ (2009)
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brugge: d-facto: 213-226.
Cancer Informatics by Prototype-networks in Mass Spectrometry
Schleif F-M, Villmann T, Kostrzewa M, Hammer B, Gammerman A (2009)
Artificial Intelligence in Medicine 45(2-3): 215-228.
Metric learning for prototype based classification
Biehl M, Hammer B, Schneider P, Villmann T (2009)
In: Innovations in Neural Information – Paradigms and Applications. Bianchini M, Maggini M, Scarselli F (Eds); Studies in Computational Intelligence, 247. Berlin: Springer: 183-199.
Fuzzy variant of affinity propagation in comparison to median fuzzy c-means
Geweniger T, Zühlke D, Hammer B, Villmann T (2009)
In: Advances in Self-Organizing Maps. Principe JC, Miikkulainen R (Eds); 72-79.
Neural Maps and Learning Vector Quantization - Theory and Applications
Schleif F-M, Villmann T (2009)
In: Proceedings of the ESANN 2009. European Symposium on Artificial Neural Networks. Advances in Computational Intelligence and Learning. Evere, Belgium: d-side publications: 509-516.
Hierarchical PCA using Tree-SOM for the Identification of Bacteria
Simmuteit S, Schleif F-M, Villmann T, Kostrzewa M (2009)
In: Advances in Self-Organizing Maps. Proceedings of the 7th International Workshop on Self Organizing Maps WSOM 2009. LNCS, 5629. Príncipe JC, Miikkulainen R (Eds); Berlin: Springer: 272-280.
Deconvolution and Identification of Mass Spectra from mixed and pure colonies of bacteria
Simmuteit S, Simmuteit J, Schleif F-M, Villmann T (2009)
In: ICOLE 2009. Blazewicz J, Ecker K, Hammer B (Eds); IfI-09-12. Clausthal-Zellerfeld, Germany: Technical University of Clausthal: 104-112.
Functional Vector Quantization by Neural Maps
Villmann T, Schleif F-M (2009)
In: Proceedings of Whispers 2009. Institute of Electrical and Electronics Engineers (Ed); Piscataway, NJ: IEEE: 636.
Similarity-based learning on structures
Biehl M, Hammer B, Hochreiter S, Kremer SC, Villmann T (Eds) (2009) ; 9081.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany.
Hyperparameter Learning in robust soft LVQ
Schneider P, Biehl M, Hammer B (2009)
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); d-side publications: 517-522.
Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data
Strickert M, Schleif F-M, Villmann T, Seiffert U (2009)
In: Similarity-based Clustering. Biehl M, Hammer B, Verleysen M, Villmann T (Eds); LNAI, 5400. Berlin: Springer: 70-91.
Advances in machine learning and computational intelligence
Schleif F-M, Biehl M, Vellido A (2009)
NeuroComputing 72(7-9): 1377-1378.
Matrix metric adaptation for improved linear discriminant analysis of biomedical data
Strickert M, Keilwagen J, Schleif F-M, T. Villmann T, Biehl M (2009)
In: Bio-Inspired Systems: Computational and Ambient Intelligence, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings. LNCS, 5517. Cabestany J, Sandoval F, Prieto A, Corchado JM (Eds); , Part 1. Berlin: Springer: 933-940.
Distance learning in discriminative vector quantization
Schneider P, Biehl M, Hammer B (2009)
Neural Computation 21(10): 2942-2969.
Patch Clustering for Massive Data Sets
Alex N, Hasenfuss A, Hammer B (2009)
Neurocomputing 72(7-9): 1455-1469.
Median topographic maps for biological data sets
Hammer B, Hasenfuss A, Rossi F (2009)
In: Similarity Based Clustering. Biehl M, Hammer B, Verleysen M, Villmann T (Eds); Lecture Notes Artificial Intelligence, 5400. Berlin, Heidelberg: Springer: 92-117.
Supervised data analysis and reliability estimation for spectral data
Schleif F-M, Villmann T, Ongyerth M (2009)
NeuroComputing 72(16-18): 3590-3601.
Stationarity of Matrix Relevance Learning Vector Quantization
Biehl M, Hammer B, Schleif F-M, Schneider P, Villmann T (2009) Machine Learning Reports.
Leipzig: Universität Leipzig.
Nonlinear dimension reduction and visualization of labeled data
Bunte K, Hammer B, Biehl M (2009)
In: International Conference on Computer Analysis of Images and Patterns. Jiang X, Petkov N (Eds); Lecture Notes in Computer Science, 5702, 5702. Berlin: Springer: 1162-1170.
Median variant of fuzzy-c-means
Geweniger T, Zühlke D, Hammer B, Villmann T (2009)
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: d-side publications: 523-528.
Graph-based Representation of Symbolic Musical Data
Mokbel B, Hasenfuss A, Hammer B (2009)
In: Graph-Based Representation in Pattern Recognition (GbRPR 2009). Lecture Notes in Computer Science, 5534. Torsello A, Escolano F, Brun L, International Association for Pattern Recognition. Technical Committee on Graph Based Representations (Eds); Lecture notes in computer science, 5534. Berlin: Springer: 42-51.
Adaptive relevance matrices in learning vector quantization
Schneider P, Biehl M, Hammer B (2009)
Neural Computation 21(12): 3532-3561.
Extended Targeted Profiling to Identify and Quantify Metabolites in 1-H NMR measurements
Schleif F-M, Riemer T, Boerner U, Cross M (2009)
In: ICOLE 2009. Blazewicz J, Ecker K, Hammer B (Eds); IfI-09-12. Clausthal-Zellerfeld, Germany: Technical University of Clausthal: 89-103.
Simmuteit S, Schleif F-M, Villmann T, Elssner T (2009)
In: Proceedings of ICMLA 2009. IEEE Press: 563--567.
Functional principal component learning using Oja's method and Sobolev norms
Villmann T, Hammer B (2009)
In: Advances in Self-Organizing Maps. Principe JC, Miikkulainen R (Eds); 325-333.
Biehl M, Hammer B, Verleysen M, Villmann T (Eds) (2009) Springer Lecture Notes Artificial Intelligence, 5400.
Berlin, Heidelberg: Springer.
Nonlinear discriminative data visualization
Bunte K, Biehl M, Hammer B (2009)
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere: d-side publications: 65-70.
Schleif F-M, Lindemann M, Maass P, Diaz M, Decker J, Elssner T, Kuhn M, Thiele H (2009)
Computing and Visualization in Science 12(4): 189-199.
Matrix Learning for Topographic Neural Maps
Arnonkijpanich B, Hammer B, Hasenfuss A, Lursinsap C (2008)
In: Artificial Neural Networks - ICANN 2008. Kůrková V, Neruda R, Koutník J (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 572-582.
Real Time Emotional Control for Anti-Swing and Positioning Control of SIMO Overhead Traveling Crane
Jamali MR, Arami A, Hosseini B, Moshiri B, Lukas C (2008)
International Journal of Innovative Computing, Information, and Control 4(9): 2333-2344.
Recurrent Neural Networks - Models, Capacities, and Applications
de Raedt L, Hammer B, Hitzler P, Maass W (Eds) (2008) ; 8041.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
Schleif F-M, Villmann T, Hammer B (2008)
In: Encyclopedia of Artificial Intelligence. Dopico JR-n R-al, Dorado J, Pazos A (Eds); IGI Global: 1337-1342.
Matrix Learning for Topographic Neural Maps
Arnonkijpanich B, Hammer B, Hasenfuss A, Lursinsap C (2008)
In: ICANN (1). Lecture Notes in Computer Science, 5163. Kurková V, Neruda R, Koutn'ık J (Eds); Berlin: Springer: 572-582.
Learning dynamics and robustness of vector quantization and neural gas
Witoelar A, Biehl M, Ghosh A, Hammer B (2008)
Neurocomputing 71(7-9): 1210-1219.
Parallelizing single pass patch clustering
Alex N, Hammer B (2008)
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Evere, Belgium: d-side publications: 227-232.
Local Matrix Adaptation in Topographic Neural Maps
Arnonkijpanich B, Hammer B, Hasenfuss A (2008) IfI-08-07.
Clausthal-Zellerfeld: Clausthal University of Technology.
Discriminative Visualization by Limited Rank Matrix Learning
Bunte K, Schneider P, Hammer B, Schleif F-M, Villmann T, Biehl M (2008) Machine Learning Reports.
Leipzig: Universität Leipzig.
Matrix Adaptation in Discriminative Vector Quantization
Schneider P, Biehl M, Hammer B (2008) IfI Technical Report Seriess.
Clausthal-Zellerfeld: Clausthal University of Technology.
Sparse coding Neural Gas for analysis of Nuclear Magnetic Resonance Spectroscopy
Schleif F-M, Ongyerth M, Villmann T (2008)
In: Proceedings of the CBMS 2008. IEEE: 620-625.
Generalized Matrix Learning Vector Quantizer for the Analysis of Spectral Data
Schneider P, Schleif F-M, Villmann T, Biehl M (2008)
In: Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN) 2008. Verleysen M (Ed); Evere, Belgium: d-side publications: 451-456.
Metric adaptation for supervised attribute rating
Strickert M, Schleif F-M, Villmann T (2008)
In: Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN) 2008. Verleysen M (Ed); Evere, Belgium: d-side publications: 31-36.
Winkler T, Drieseberg J, Hasenfuß A, Hammer B, Hormann K (2008)
In: Proceedings of Vision, Modeling, and Visualization 2008. Deussen O, Keim D, Saupe D (Eds); Konstanz, Germany: Aka: 149-158.
Topographic processing of very large text datasets
Hasenfuss A, Boerger W, Hammer B (2008)
In: Smart Systems Engineering: Computational Intelligence in Architecting Systes (ANNIE 2008). Daglie CH (Ed); ASME Press: 525-532.
Single Pass Clustering and Classification of Large Dissimilarity Datasets
Hasenfuss A, Hammer B (2008)
In: Artificial Intelligence and Pattern Recognition. Prasad B, Sinha P, Ram A, Kerre EE (Eds); ISRST: 219-223.
Prototype based Fuzzy Classification in Clinical Proteomics
Schleif F-M, Villmann T, Hammer B (2008)
International Journal of Approximate Reasoning 47(1): 4-16.
Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics
Strickert M, Schneider P, Keilwagen J, Villmann T, Biehl M, Hammer B (2008)
In: Artificial Neural Networks in Pattern Recognition. Third IAPR Workshop. Proceedings. Prevost L, Marinai S, Schwenker F (Eds); Lecture Notes in Computer Science, 5064. Berlin: Springer: 78-89.
Robust Centroid-Based Clustering using Derivatives of Pearson Correlation
Strickert M, Sreenivasulu N, Villmann T, Hammer B (2008)
In: BIOSIGNALS (2). Encarnação P, Veloso A (Eds); INSTICC - Institute for Systems and Technologies of Information, Control and Communication: 197-203.
Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets
Hasenfuss A, Hammer B, Rossi F (2008)
In: Artificial Neural Networks in Pattern Recognition, Third IAPR Workshop. Proceedings. Lecture Notes in Computer Science, 5064. Prevost L, Marinai S, Schwenker F (Eds); Berlin: Springer: 1-12.
Analysis of Spectral Data in Clinical Proteomics by use of Learning Vector Quantizers
Schleif F-M, Hammer B, Villmann T (2008)
In: Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications. Van de Werff M, Delder A, Tollenaar R (Eds); Berlin: Springer: 141-167.
Automatic Identification and Quantification of Metabolites in H-NMR Measurements
Schleif F-M, Riemer T, Cross M, Villmann T (2008)
In: Proceedings of the Workshop on Computational Systems Biology (WCSB) 2008. 165-168.
Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data
Strickert M, Schleif F-M, Seiffert U (2008)
Ibero-American Journal of Artificial Intelligence 37(12): 37-44.
Villmann T, Schleif F-M, Kostrzewa M, Walch A, Hammer B (2008)
Briefings in Bioinformatics 9(2): 129-143.
Magnification Control in Relational Neural Gas
Hasenfuss A, Hammer B, Geweniger T, Villmann T (2008)
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels: d-side publications: 325-330.
Fuzzy Classification Using Information Theoretic Learning Vector Quantization
Villmann T, Hammer B, Schleif F-M, Hermann W, Cottrell M (2008)
Neurocomputing 71(16-18): 3070-3076.
Comparison of cluster algorithms for the analysis of text data using Kolmogorov complexity
Geweniger T, Schleif F-M, Hasenfuss A, Hammer B, Villmann T (2008)
In: ICONIP 2008. Köppen M, Kasabov NK, Coghill GG (Eds); Berlin, Heidelberg: Springer: 61-69.
Accelerating Relational Clustering Algorithms With Sparse Prototype Representation
Rossi F, Hasenfuß A, Hammer B (2007)
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data
Schneider P, Biehl M, Schleif F-M, Hammer B (2007)
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
Class imaging of hyperspectral satellite remote sensing data using FLSOM
Villmann T, Schleif F-M, Merenyi E, Strickert M, Hammer B (2007)
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
Learning Vector Quantization: generalization ability and dynamics of competing prototypes
Witoelar A, Biehl M, Hammer B (2007)
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
Single pass clustering for large data sets
Alex N, Hammer B, Klawonn F (2007)
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
Topographic Processing of Relational Data
Hammer B, Hasenfuß A, Rossi F, Strickert M (2007)
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
Intuitive Clustering of Biological Data
Hammer B, Hasenfuss A, Schleif F-M, Villmann T, Strickert M, Seiffert U (2007)
In: Proceedings of International Joint Conference on Neural Networks. IEEE: 1877-1882.
Hasenfuss A, Hammer B (2007)
In: Advances in Intelligent Data Analysis VII, Proceedings of the 7th International Symposium on Intelligent Data Analysis. Berthold MR, Shawe-Taylor J, Lavrac N (Eds); , 4723. Berlin: Springer: 93-105.
Advances in pre-processing and model generation for mass spectrometric data analysis
Schleif F-M (2007)
In: Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings. Biehl M, Hammer B, Verleysen M, Villmann T (Eds); (07131). Dagstuhl, Germany: Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany.
Aggregation of multiple peak lists by use of an improved neural gas network
Schleif F-M, Hasenfuss A, Hammer B (2007)
Leipzig: Universität Leipzig.
Derivatives of Pearson Correlation for Gradient based Analysis of Biomedical Data
Strickert M, Schleif F-M, Villmann T, Seiffert U (2007)
In: Similarity based Clustering. Lecture Notes in Artificial Intelligence, 5400., 12(37). IBERAMIA: Sociedad Iberoamericana de Inteligencia Artificial.
Dynamics and generalization ability of LVQ algorithms
Biehl M, Ghosh A, Hammer B (2007)
Journal of Machine Learning Research 8: 323-360.
Hammer B, Hasenfuss A (2007) IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
Neural Gas for Surface Reconstruction
Melato M, Hammer B, Hormann K (2007) IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing-Maps
Schleif F-M, Villmann T, Hammer B (2007)
In: Application of Fuzzy Sets Theory. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications. LNAI 4578. Masulli F, Mitra S, Pasi G (Eds); Berlin, Heidelberg: Springer: 563-570.
Schneider P, Biehl M, Hammer B (2007)
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 37-42.
Prototypen basiertes maschinelles Lernen in der klinischen Proteomik
Schleif F-M (2007)
In: Ausgezeichnete Informatikdissertationen 2006. Wagner D (Ed); GI-Edition Lecture Notes in Informatics. Dissertation, 7. Bonn: Gesellschaft für Informatik: 179-188.
Margin based Active Learning for LVQ Networks
Schleif F-M, Hammer B, Villmann T (2007)
Neurocomputing 70(7-9): 1215-1224.
Blazewicz J, Ecker K, Hammer B (2007)
Clausthal-Zellerfeld: Clausthal University of Technology.
Maschinelles Lernen mit Prototypmethoden in der klinischen Proteomik
Schleif F-M (2007)
KI - Künstliche Intelligenz (4): 65-67.
Association learning in SOMs for Fuzzy-Classification
Villmann T, Schleif F-M, v.d.Werff M, Deelder A, Tollenaar R (2007)
In: 6th International Conference on Machine Learning and Applications, 2007. 581-586.
On the dynamics of vector quantization and neural gas
Witolaer A, Biehl M, Ghosh A, Hammer B (2007)
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007). Verleysen M (Ed); Brussels, Belgium: d-side publications: 127-132.
Similarity-based Clustering and its Application to Medicine and Biology
Biehl M, Hammer B, Verleysen M, Villmann T (Eds) (2007) ; 7131.
Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI).
Perspectives of Neural-Symbolic Integration
Hammer B, Hitzler P (Eds) (2007) Studies in Computational Intelligence, 77.
Berlin: Springer.
How to process uncertainty in machine learning
Hammer B, Villmann T (2007)
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2007). Verleysen M (Ed); Brussels, Belgium: d-side publications: 79-90.
Neural gas clustering for dissimilarity data with continuous prototypes
Hasenfuss A, Hammer B, Schleif F-M, Villmann T (2007)
In: Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin: Springer: 539-546.
Visualization of fuzzy information in in fuzzy-classification for image sagmentation using MDS
Villmann T, Strickert M, Brüß C, Schleif F-M, Seiffert U (2007)
In: Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN) 2007. Verleysen M (Ed); Evere, Belgium: d-side publications: 103-108.
Statistical Classification and Visualization of MALDI-Imaging Data
Deininger S-O, Gerhard M, Schleif F-M (2007)
In: Proc. of CBMS 2007. 403-405.
Markovian Bias of Neural-based Architectures With Feedback Connections
Tino P, Hammer B, Boden M (2007)
In: Perspectives of Neural-Symbolic Integration. Hammer B, Hitzler P (Eds); Studies in computational Intelligence, 77. Berlin: Springer: 95-134.
Fuzzy Labeled Self Organizing Map for Clasification of Spectra
Villmann T, Schleif F-M, Merenyi E, Hammer B (2007)
In: Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin: Springer: 556-563.
Hammer B, Hasenfuss A (2007)
In: KI 2007: Advances in Artificial Intelligence. Lecture Notes in Artificial Intelligence, 4667. Hertzberg J, Beetz M, Englert R (Eds); Berlin: Springer: 190-204.
Magnification control for batch neural gas
Hammer B, Hasenfuss A, Villmann T (2007)
Neurocomputing 70(7-9): 1225-1234.
Hammer B, Micheli A, Sperduti A (2007)
In: Perspectives of Neural-Symbolic Integration. Hammer B, Hitzler P (Eds); Studies in computational Intelligence, 77. Berlin: Springer: 67-94.
Neural gas clustering for sparse proximity data
Hasenfuss A, Hammer B, Schleif F-M, Villmann T (2007)
In: Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin, Heidelberg, Germany: Springer: 539-546.
Schleif F-M, Hammer B, Villmann T (2007)
In: Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507. Sandoval F, Prieto A, Cabestany J, Grana M (Eds); Berlin, Heidelberg: Springer: 1036-1044.
Preprocessing of Nuclear Magnetic Resonance Spectrometry Data
Schleif F-M (2007) Machine Learning Reports.
Leipzig: Universität Leipzig.
Supervised Attribute Relevance Determination for Protein Identification in Stress Experiments
Strickert M, Schleif F-M (2007)
In: Proc. of MLSB 2007. 81-86.
Gradients of Pearson Correlation for Analysis of Biomedical Data
Strickert M, Schleif F-M, Seiffert U (2007)
In: Proc. of ASAI 2007. 139-150.
Prototype based classification using information theoretic learning
Villmann T, Hammer B, Schleif F-M, Geweniger T, Fischer T, Cottrell M (2006)
In: Neural Information Processing, 13th International Conference. Proceedings. King I, Wang J, Chan L, Wang DLL (Eds); Lecture Notes in Computer Science, 4233, Part II. Berlin: Springer: 40-49.
Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes
Villmann T, Seiffert U, Schleif F-M, Brüß C, Geweniger T, Hammer B (2006)
In: Proceedings of Conference Artificial Neural Networks in Pattern Recognition. Schwenker F (Ed); Berlin: Springer: 46-56.
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
In: Proceedings of Conference Artificial Neural Networks in Pattern Recognition (ANNPR). Schwenker F (Ed); Berlin: Springer Verlag: 33-45.
Margin based Active Learning for LVQ Networks
Schleif F-M, Hammer B, Villmann T (2006)
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 539-544.
Prototype based Machine Learning for Clinical Proteomics
Schleif F-M (2006)
Clausthal-Zellerfeld, Germany: Technical University Clausthal.
Machine Learning and Soft-Computing in Bioinformatics. A Short Journey
Schleif F-M, Elssner T, Kostrzewa M, Villmann T, Hammer B (2006)
In: Proc. of FLINS 2006. World Scientific Press: 541-548.
Matrix Learning in Learning Vector Quantization
Biehl M, Hammer B, Schneider P (2006)
Clausthal-Zellerfeld: Clausthal University of Technology.
Cottrell M, Hammer B, Hasenfuss A, Villmann T (2006)
Neural Networks 19(6-7): 762-771.
Perspectives of Self-adapted Self-organizing Clustering in Organic Computing
Villmann T, Hammer B, Seiffert U (2006)
In: Biologically Inspired Approaches to Advanced Information Technology, Second International Workshop. Proceedings. Lecture Notes in Computer Science, 3853. Ijspeert AJ, Masuzawa T, Kusumoto S (Eds); Berlin: Springer: 141-159.
Villmann T, Schleif F-M, Hammer B (2006)
Neural Networks 19(5): 610-622.
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
In: Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16. Dagli C, Buczak A, Enke D, Embrechts A, Ersoy O (Eds); ASME Press: 623-633.
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006)
In: Smart systems engineering : infra-structure systems engineering, bio-informatics and computational biology and evolutionary computation : proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2006). Dagli CH (Ed); ASME Press series on intelligent engineering systems through artificial neural networks, 16. New York, NY: ASME Press: 623-632.
Analysis and Visualization of Proteomic Data by Fuzzy labeled Self-Organizing Maps
Schleif F-M, Elssner T, Kostrzewa M, Villmann T, Hammer B (2006)
In: 19th IEEE International Symposium on Computer- based Medical Systems. Lee DJ, Nutter B, Antani S, Mitra S, Archibald J (Eds); Los Alamitos: IEEE Computer Society Press: 919-924.
Neural Networks and Machine Learning in Bioinformatics - Theory and Applications
Seiffert U, Hammer B, Kaski S, Villmann T (2006)
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels, Belgium: d-side publications: 521-532.
Fuzzy Image Segmentation with Fuzzy Labelled Neural Gas
Brüß C, Bollenbeck F, Schleif F-M, Weschke W, Villmann T, Seiffert U (2006)
In: Proc. of ESANN 2006. 563-569.
Fuzzy Classification by Fuzzy Labeled Neural Gas
Villmann T, Hammer B, Schleif F-M, Geweniger T, Herrmann W (2006)
Neural Networks 19(6-7): 772-779.
Prototype-based fuzzy classification with local relevance for proteomics
Villmann T, Schleif F-M, Hammer B (2006)
Neurocomputing 69(16-18): 2425-2428.
Learning vector quantization: The dynamics of winner-takes-all algorithms
Biehl M, Ghosh A, Hammer B (2006)
Neurocomputing 69(7-9): 660-670.
Performance analysis of LVQ algorithms: a statistical physics approach
Ghosh A, Biehl M, Hammer B (2006)
Neural Networks 19(6-7): 817-829.
Hammer B, Hasenfuss A, Schleif F-M, Villmann T (2006) IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
Magnification Control for Batch Neural Gas
Hammer B, Hasenfuss A, Villmann T (2006)
In: Proc. Of European Symposium on Artificial Neural Networks. Verleysen M (Ed); Brussels: d-side publications: 7-12.
On the capacity of unsupervised recursive neural networks for symbol processing
Hammer B, Neubauer N (2006)
In: Workshop proceedings of NeSy'06. d'Avila Garcez A, Hitzler P, Tamburrini G (Eds); .
Effizient Klassifizieren und Clustern: Lernparadigmen von Vektorquantisierern
Hammer B, Villmann T (2006)
Künstliche Intelligenz 3(6): 5-11.
Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis
Strickert M, Seiffert U, Sreenivasulu N, Weschke W, Villmann T, Hammer B (2006)
Neurocomputing 69(7-9): 651-659.
Learning vector quantization classification with local relevance determination for medical data
Hammer B, Villmann T, Schleif F-M, Albani C, Hermann W (2006)
In: Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029. Rutkowski L, Tadeusiewicz R, Zadeh LA, Zurada J (Eds); Lecture notes in computer science ; 4029 : Lecture notes in artificial intelligence, 4029. Berlin, Heidelberg: Springer: 603-612.
Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning
Villmann T, Schleif F-M, Hammer B (2005)
In: Fourth International Conference on Machine Learning and Applications (ICMLA'05). IEEE: 11-15.
Fuzzy Labeled Neural GAS for Fuzzy Classification
Villmann T, Hammer B, Schleif F-M, Geweniger T (2005)
In: Proceedings of the 5th Workshop on Self-Organizing Maps [on CD-ROM]. Cottrell M (Ed); Paris, France: University Paris-1-Pantheon-Sorbonne: 283-290.
Self Organizing Maps for Time Series
Hammer B, Micheli A, Neubauer N, Sperduti A, Strickert M (2005)
In: Proceedings of WSOM 2005. 115-122.
Strickert M, Hammer B (2005)
Neurocomputing 64: 39-71.
Schleif F-M (2005)
Offene Systeme 2005(1): 5-10.
Villmann T, Schleif F-M, Hammer B (2005)
In: International Workshop on Integrative Bioinformatics.
The dynamics of Learning Vector Quantization
Biehl M, Gosh A, Hammer B (2005)
In: ESANN'05. Verleysen M (Ed); Evere: d-side publishing: 13-18.
Cottrell M, Hammer B, Hasenfuss A, Villmann T (2005)
In: Proceedings of WSOM 2005. 275-282.
On approximate learning by multi-layered feedforward circuits
DasGupta B, Hammer B (2005)
Theoretical Computer Science 348(1): 95-127.
Dynamical Analysis of LVQ type learning rules
Ghosh A, Biehl M, Hammer B (2005)
In: Proceedings of WSOM. 578-594.
Universal approximation capability of cascade correlation for structures
Hammer B, Micheli A, Sperduti A (2005)
Neural Computation 17(5): 1109-1159.
Relevance learning for mental disease classification
Hammer B, Rechtien A, Strickert M, Villmann V (2005)
In: ESANN'05. Verleysen M (Ed); d-side publishing: 139-144.
Relevance determination in reinforcement learning
Tluk von Toschanowitz K, Hammer B, Ritter H (2005)
In: ESANN'05. Verleysen M (Ed); d-side publishing: 369-374.
New Aspects in Neurocomputing.
Cottrell M, Hammer B, Villmann T (2005)
Neurocomputing 63: 1-3.
Improving iterative repair strategies for scheduling with the SVM
Gersmann K, Hammer B (2005)
Neurocomputing 63: 271-292.
On the Generalization Ability of Prototype-Based Classifiers with Local Relevance Determination
Hammer B, Schleif F-M, Villmann T (2005) IfI Technical reports.
Clausthal-Zellerfeld: Clausthal University of Technology.
Supervised neural gas with general similarity measure
Hammer B, Strickert M, Villmann T (2005)
Neural Processing Letters 21(1): 21-44.
Fuzzy labeled soft nearest neighbor classification with relevance learning
Villmann T, Schleif F-M, Hammer B (2005)
In: Proceedings of the International Conference of Machine Learning Applications. Wani MA, Cios KJ, Hafeez K (Eds); Los Angeles: IEEE Press: 11-15.
Special issue on neural networks and kernel methods for structured domains
Hammer B, Saunders C, Sperduti A (2005)
Neural Networks 18(8): 1015-1018.
Prototype based recognition of splice sites
Hammer B, Strickert M, Villmann T (2005)
In: Bioinformatics using computational intelligence paradigms. Seiffert U, Jain LC, Schweitzer P (Eds); Berlin: Springer: 25-55.
Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data
Schleif F-M, Villmann T, Hammer B (2005)
In: Proceedings of the 6th Workshop on Fuzzy Logic and Applications. Bloch I, Petrosino A, Tettamanzi AGB (Eds); Berlin, Heidelberg: Springer: 290-296.
On the generalization ability of GRLVQ networks
Hammer B, Strickert M, Villmann T (2005)
Neural Processing Letters 21(2): 109-120.
Classification using non standard metrics
Hammer B, Villmann T (2005)
In: ESANN'05. Verleysen M (Ed); Brussels: d-side publishing: 303-316.
Unsupervised recursive sequences processing
Strickert M, Hammer B, Blohm S (2005)
Neurocomputing 63: 69-97.
A reinforcement learning algorithm to improve scheduling search heuristics with the SVM
Gersmann K, Hammer B (2004)
In: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)., 3. IEEE: 1811-1816.
A mathematical characterization of the architectural bias of recursive models
Hammer B, Tino P, Micheli A (2004) Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
Villmann T, Hammer B, Schleif F-M (2004)
In: Proceedings of Selbstorganisation Von Adaptivem Verfahren. Fortschritts-Berichte VDI Reihe 10, Nr. 742. Groß H-M, Debes K, Böhme H-J (Eds); VDI Verlag: 592-597.
Villmann T, Schleif F-M, Hammer B (2004)
In: SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. Groß H-M, Debes K, Böhme H-J (Eds); VDI Verlag.
Mapping the Design Space of Reinforcement Learning Problems - a Case Study
Tluk von Toschanowitz K, Hammer B, Ritter H (2004)
In: SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. Gross H-M, Debes K, Böhme H-J (Eds); VDI Verlag: 251-261.
Neural methods for non-standard data
Hammer B, Jain BJ (2004)
In: European Symposium on Artificial Neural Networks'2004. Verleysen M (Ed); D-side publications: 281-292.
Recursive self-organizing network models
Hammer B, Micheli A, Sperduti A, Strickert M (2004)
Neural Networks 17(8-9): 1061-1085.
Hammer B, Strickert M, Villmann T (2004)
In: Artificial Intelligence and Softcomputing, Lecture Notes in Artificial Intelligence, 3070. Rutkowski L, Siekmann J, Tadeusiewicz R, Zadeh LA (Eds); Berlin: Springer: 592-597.
On early stages of learning in connectionist models with feedback connections
Tino P, Hammer B (2004)
In: Compositional Connectionism in Cognitive Science.
A reinforcement learning algorithm to improve scheduling search heuristics with the SVM
Gersmann K, Hammer B (2004)
In: IJCNN.
A general framework for unsupervised processing of structured data
Hammer B, Micheli A, Sperduti A, Strickert M (2004)
Neurocomputing 57: 3-35.
Schleif F-M, Clauss U, Villmann T, Hammer B (2004)
In: Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004. Wani MA, Cios KJ, Hafeez K (Eds); Los Alamitos, CA, USA: IEEE Press: 374-379.
Self-organizing context learning
Strickert M, Hammer B (2004)
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-side publications: 39-44.
Recurrent Neural Networks with Small Weights Implement Definite Memory Machines
Hammer B, Tiňo P (2003)
Neural Computation 15(8): 1897-1929.
Neural maps in remote sensing image analysis
Villmann T, Merényi E, Hammer B (2003)
Neural Networks 16(3-4): 389-403.
Architectural Bias in Recurrent Neural Networks: Fractal Analysis
Tiňo P, Hammer B (2003)
Neural Computation 15(8): 1931-1957.
Architectural Bias in Recurrent Neural Networks: Fractal Analysis
Tiño P, Hammer B (2003)
Neural Computation 15(8): 1931-1957.
Supervised Neural Gas and Relevance Learning in Learning Vector Quantization
Villmann T, Schleif F-M, Hammer B (2003)
In: Proceedings of the 4th Workshop on Self Organizing Maps [on CD-ROM]. Yamakawa T (Ed); Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology: 47-52.
On the generalization ability of GRLVQ
Hammer B, Strickert M, Villmann T (2003) Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
Monitoring technical systems with prototype based clustering
Bojer T, Hammer B, Koeers C (2003)
In: ESANN 2003, 10th European Symposium on Artificial Neural Network. Proceedings. Verleysen M (Ed); Evere: D-side publication: 433-439.
A Note on the Universal Approximation Capability of Support Vector Machines
Hammer B, Gersmann K (2003)
Neural Processing Letters 17(1): 43-53.
Perspectives on learning symbolic data with connectionistic systems
Hammer B (2003)
In: Adaptivity and Learning. Kühn R, Menzel R, Menzel W, Ratsch U, Richter MM, Stamatescu I (Eds); Berlin: Springer: 141-160.
Mathematical Aspects of Neural Networks
Hammer B, Villmann T (2003)
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2003). Verleysen M (Ed); Brussels, Belgium: d-side: 59-72.
A mission for the EEG coherence analysis: Is the task complex or difficult?
Köhler M, Buchta K, Schleif F-M, Sommerfeld E (2003)
Brain Topography 15(4): 271.
Unsupervised recursive sequence processing
Strickert M, Hammer B (2003)
In: 10th European Symposium on Artificial Neural Networks. Proceedings. Verleysen M (Ed); D-side publication: 27-32.
Strickert M, Hammer B (2003)
In: WSOM'03. 53-57.
Working memory load and EEG coherence
Dörfler T, Simmel A, Schleif F-M, Sommerfeld E (2003)
Brain Topography 15(4): 269.
Improving iterative repair strategies for scheduling with the SVM
Gersmann K, Hammer B (2003)
In: ESANN 2003, 10th European Symposium on Artificial Neural Networks. Proceedings. Verleysen M (Ed); Evere: D-side publication: 235-240.
A general framework for self-organizing structure processing neural networks
Hammer B, Micheli a., Sperduti A (2003)
Pisa: Universita di Pisa, Dipartimento die Informatica.
Metric adaptation and relevance learning in learning vector quantization
Villmann T, Hammer B (2003) Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
Neural maps in remote sensing image analysis
Villmann T, Merényi E, Hammer B (2003)
Neural Networks 16(3-4): 389-403.
Determining Relevant Input Dimensions for the Self-Organizing Map
Bojer T, Hammer B, Strickert M, Villmann T (2003)
In: Neural Networks and Soft Computing (Proc. ICNNSC 2002). Rutkowski L, Kacprzyk J (Eds); Physica-Verlag: 388-393.
Recurrent Neural Networks with Small Weights Implement Definite Memory Machines
Hammer B, Tiño P (2003)
Neural Computation 15(8): 1897-1929.
A distributed logistic support communication system
Gruhn V, Hülder M, Ijoui R, Schleif F-M (2003)
In: Proceedings of ISD 2003 - Constructing the Infrastructure for the Knowledge Economy - Methods and Tools, Theory and Practice. Linger H, Fisher J, Wojtkowski WG, Zupancic J, Vigo K, Arnold J (Eds); London: Kluwer Academic Publishers: 705-713.
Recurrent networks for structured data – A unifying approach and its properties
Hammer B (2002)
Cognitive Systems Research 3(2): 145-165.
Generalized relevance learning vector quantization
Hammer B, Villmann T (2002)
Neural Networks 15(8-9): 1059-1068.
Supervised Neural Gas for Learning Vector Quantization
Villmann T, Hammer B (2002)
In: Proc. of the 5th German Workshop on Artificial Life. Polani D, Kim J, Martinetz T (Eds); Berlin: Akademische Verlagsgesellschaft - infix - IOS Press: 9-16.
A general framework for unsupervised processing of structured data
Hammer B, Micheli A, Sperduti A (2002)
In: ESANN 2002, 10th European Symposium on Artificial Neural Network. Proceedings. Verleysen M (Ed); De-side publication: 389-394.
Architectural bias in recurrent neural networks – fractal analysis
Tino P, Hammer B (2002)
In: Proc. International Conf. on Artificial Neural Networks. Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer: 370-376.
{LaTeX} im studentischen Alltag
Schleif F-M, Stamer H (2002)
Gaotenblatt: 3-10.
Perspectives on Learning with Recurrent Neural Networks
Hammer B, Steil JJ (2002)
In: Proc. European Symposium Artificial Neural Networks. Verleysen M (Ed); D-side publication: 357-368.
Hammer B, Villmann T (2002)
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2002). Verleysen M (Ed); Brussels, Belgium: d-side: 295-300.
Generalized Relevance Learning Vector Quantization
Hammer B, Villmann T (2002)
Neural Networks 15(8-9): 1059-1068.
Complexity and difficulty in memory based comparison
Köhler M, Buchta K, Schleif F-M, Sommerfeld E (2002)
In: Proceedings of the 18th Meeting of the International Society for Psychophysics. da Silva JA, Filho NPR, Matsushima EH (Eds); Pabst Publishing: 433-439.
OCR mit statistischen Momenten
Schleif F-M (2002)
Gaotenblatt 2002: 15-17.
Compositionality in Neural Systems
Hammer B (2002)
In: Handbook of Brain Theory and Neural Networks. Arbib M (Ed); 2nd. MIT Press: 244-248.
Recurrent neural networks for structured data – a unifying approach and its properties
Hammer B (2002)
Cognitive Systems Research 3(2): 145-165.
Learning Vector Quantization for Multimodal Data
Hammer B, Strickert M, Villmann T (2002)
In: Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer Verlag: 370-376.
Rule Extraction from Self-Organizing Networks
Hammer B, Strickert M, Villmann T (2002)
In: Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Dorronsoro JR (Ed); Berlin: Springer Verlag: 877-883.
Neural networks with small weights implement finite memory machines
Hammer B, Tino P (2002) Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
On the Generalization Ability of Recurrent Networks
Hammer B (2001)
In: Artificial Neural Networks — ICANN 2001. Dorffner G, Bischof H, Hornik K (Eds); Lecture Notes in Computer Science, 2130. Berlin, Heidelberg: Springer Berlin Heidelberg: 731-736.
Generalized Relevance LVQ for Time Series
Strickert M, Bojer T, Hammer B (2001)
In: Artificial Neural Networks — ICANN 2001. Dorffner G, Bischof H, Hornik K (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 677-683.
Generalization ability of folding networks
Hammer B (2001)
IEEE Transactions on Knowledge and Data Engineering 13(2): 196-206.
Vidyasagar M, Balaji S, Hammer B (2001)
Systems & Control Letters 42(2): 151-157.
Input Pruning for Neural Gas Architectures
Hammer B, Villmann T (2001)
In: Proc. Of European Symposium on Artificial Neural Networks (ESANN'2001). Brussels, Belgium: D facto publications: 283-288.
Relevance determination in learning vector quantization
Bojer T, Hammer B, Schunk D, Tluk von Toschanowitz K (2001)
In: ESANN'2001. Verleysen M (Ed); D-facto publications: 271-276.
Vidyasagar M, Balaji S, Hammer B (2001)
System and Control Letters 42: 151-157.
On the Generalization Ability of Recurrent Networks
Hammer B (2001)
In: Artificial Neural Networks. Proceedings. Lecture Notes in Computer Science, 2130. Dorffner G, Bischof H, Hornik K (Eds); Berlin: Springer: 731-736.
Estimating Relevant Input Dimensions for Self-Organizing Algorithms
Hammer B, Villmann T (2001)
In: Advances in Self-Organising Maps. Allinson NM, Yin H, Allinson L, Slack J (Eds); London: Springer: 173-180.
Generalized Relevance LVQ for Time Series
Strickert M, Bojer T, Hammer B (2001)
In: Artificial Neural Networks. International Conference. Proceedings. Lecture Notes in Computer Science, 2130. Dorffner G, Bischof H, Hornik K (Eds); Berlin: Springer: 677-683.
Simmel A, Dörfler T, Schleif F-M, Sommerfeld E (2001)
In: Proceedings of the 17th Meeting of the International Society for Psychophysics. Pabst Publishing: 602-607.
Generalization Ability of Folding Networks.
Hammer B (2001)
IEEE Trans. Knowl. Data Eng. 13(2): 196-206.
Complexity - dependent synchronization of brain subsystems during memorization
Dörfler T, Simmel A, Schleif F-M, Sommerfeld E (2001)
In: Proceedings of the 17th Meeting of the International Society for Psychophysics. Pabst Publishing: 343-348.
On the approximation capability of recurrent neural networks
Hammer B (2000)
Neurocomputing 31(1-4): 107-123.
Hammer B (2000)
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 213-218.
Learning with Recurrent Neural Networks
Hammer B (2000) Lecture Notes in Control and Information Sciences, 254.
Berlin: Springer.
On the approximation capability of recurrent neural networks
Hammer B (2000)
Neurocomputing 31(1-4): 107-123.
On Approximate Learning by Multi-layered Feedforward Circuits.
DasGupta B, Hammer B (2000)
In: Algorithmic Learning Theory, 11th International Conference. Proceedings. Lecture Notes in Computer Science, 1968. Arimura H, Jain S, Sharma A (Eds); Berlin: Springer: 264-278.
Approximation and generalization issues of recurrent networks dealing with structured data
Hammer B (2000)
In: ECAI workshop: Foundations of connectionist-symbolic integration: representation, paradigms, and algorithms. Frasconi P, Sperduti A, Gori M (Eds); .
Neural networks classifying symbolic data
Hammer B (2000)
In: ICML workshop on attribute-value and relational learning: crossing the boundaries. de Raedt L, Kramer S (Eds); 61-65.
On the Learnability of Recursive Data
Hammer B (1999)
Mathematics of Control, Signals, and Systems 12(1): 62-79.
On the learnability of recursive data
Hammer B (1999)
Mathematics of Control, Signals and Systems 12: 62-79.
Approximation capabilities of folding networks
Hammer B (1999)
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 33-38.
Hardness of approximation of the loading problem for multi-layered feedforward neural networks
DasGupta B, Hammer B (1999)
DIMACS Center, Rutgers University.
On the Approximation Capability of Recurrent Neural Networks
Hammer B (1998)
In: Proceedings of the International ICSC / IFAC Symposium on Neural Computation (NC 1998). Heiss M (Ed); ICSC Academic Press: 512-518.
Training a sigmoidal network is difficult
Hammer B (1998)
In: European Symposium on Artificial Neural Networks. Verleysen M (Ed); D-facto publications: 255-260.
Some complexity results for perceptron networks
Hammer B (1998)
In: International Conference on artificial Neural Networks. 639-644.
Generalization of Elman Networks
Hammer B (1997)
In: Artificial Neural Networks - ICANN '97, 7th International Conference. Proceedings. Lecture Notes in Computer Science, 1327. Berlin: Springer: 409-414.
Neural networks can approximate mappings on structured objects
Hammer B, Sperschneider V (1997)
In: International conference on Computational Intelligence and Neural Networks. Wang PP (Ed); 211-214.
On the generalization ability of simple recurrent neural networks
Hammer B (1997) Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
Learning recursive data is intractable
Hammer B (1997) Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
A NP-hardness result for a sigmoidal 3-node neural network
Hammer B (1997) Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
Universal approximation of mappings on structured objects using the folding architecture
Hammer B (1996) Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.
Theoretische Informatik. Eine problemorientierte Einführung
Sperschneider V, Hammer B (1996)
erlin: Springer.