Hammer Lab, Bielefeld University

Challenges, Methods, Data–A Survey of Machine Learning in Water Distribution Networks

Vaquet V, Hinder F, Artelt A, Ashraf I, Strotherm J, Vaquet J, Brinkrolf J, Hammer B (2024)
In: Artificial Neural Networks and Machine Learning – ICANN 2024. 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part IX. Wand M, Malinovská K, Schmidhuber J, Tetko IV (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 155-170.

TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models

Velioglu R, Bevandic P, Chan R, Hammer B (2024)
arXiv:2411.18350.

Vieth M, Grimmelsmann N, Schneider A, Hammer B (2024)
In: ESANN 2024 proceedings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 221-226.

Explaining Neural Networks - Deep and Shallow

Hammer B (2024)
In: Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond (WSOM+ 2024) . Villmann T, Kaden M, Geweniger T, Schleif F-M (Eds); Lecture Notes in Networks and Systems, 1087. Cham: Springer : 139-140.

SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification

Kolpaczki P, Muschalik M, Fumagalli F, Hammer B, Hüllermeier E (2024)
In: International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain. Dasgupta S, Mandt S, Li Y (Eds); Proceedings of Machine Learning Research, 238. San Diego: PMLR: 3520-3528.

EPyT-Flow: A Toolkit for Generating Water DistributionNetwork Data

Artelt A, Kyriakou MS, Vrachimis SG, Eliades DG, Hammer B, Polycarpou MM (2024)
Journal of Open Source Software 9(103): 7104.

FairGLVQ: Fairness in Partition-Based Classification

Störck F, Hinder F, Brinkrolf J, Paaßen B, Vaquet V, Hammer B (2024)
In: Proceedings of the 15th International Workshop on Self-Organizing Maps (WSOM 2024). Villmann T, Kaden M, Geweniger T, Schleif F-M (Eds); Cham: Springer Nature Switzerland: 141-151.

Novel approach for data-driven modelling of multi-stage straightening and bending processes

Peters H, Djakow E, Rostek T, Mazur A, Trächtler A, Homberg W, Hammer B (2024)
In: Material Forming: ESAFORM 2024. Materials Research Proceedings, 41. Materials Research Forum LLC: 2289-2298.

Visualizing and Improving 3D Mesh Segmentation with DeepView

Mazur A, Roberts J (I), Leins D, Schulz A, Hammer B (2024)
In: ESANN 2024 proceedings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 649-654.

Image restoration in frequency space using complex-valued CNNs

Shah ZH, Müller M, Hübner W, Ortkraß H, Hammer B, Huser T, Schenck W (2024)
Frontiers in Artificial Intelligence 7: 1353873.

Noise Robust One-Class Intrusion Detection on Dynamic Graphs

Liuliakov A, Schulz A, Hermes L, Hammer B (2024)
In: ESANN 2024 proceedings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 363-368.

One or two things we know about concept drift—a survey on monitoring in evolving environments. Part B: locating and explaining concept drift

Hinder F, Vaquet V, Hammer B (2024)
Frontiers in Artificial Intelligence 7.

Self-Supervised Learning from Incrementally Drifting Data Streams

Vaquet V, Vaquet J, Hinder F, Malialis K, Panayiotou C, Polycarpou M, Hammer B (2024)
In: ESANN 2024 proceesdings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 431-436.

On the Fine Structure of Drifting Features

Hinder F, Vaquet V, Hammer B (2024)
In: ESANN 2024 proceesdings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 63-68.

Causes of Rejects in Prototype-based Classification Aleatoric vs. Epistemic Uncertainty

Brinkrolf J, Vaquet V, Hinder F, Hammer B (2024)
In: ESANN 2024 proceesdings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com: 191-196.

Localizing of Anomalies in Critical Infrastructure using Model-Based Drift Explanations

Vaquet V, Hinder F, Vaquet J, Lammers K, Quakernack L, Hammer B (2024)
In: 2024 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-8.

Fairness-enhancing classification methods for non-binary sensitive features—How to fairly detect leakages in water distribution systems

Strotherm J, Ashraf MI, Hammer B (2024)
PeerJ Computer Science 10: e2317.

Sustainable Life-Cycle of Intelligent Socio-Technical Systems

Hammer B, Alaçam Ö, Arlinghaus CS, Brinkmann M, Dörksen H, Hoeken S, Jungeblut T, Knaup J, Leite D, Lohweg V, Maier GW, et al. (2024) .

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.

Fairness in KI-Systemen

Strotherm J, Müller A, Hammer B, Paaßen B (2024)
In: Vertrauen in Künstliche Intelligenz. Eine multi-perspektivische Betrachtung. Schork S (Ed); Wiesbaden: Springer Fachmedien Wiesbaden: 163-183.

The SAME score: Improved cosine based measure for semantic bias

Schroeder S, Schulz A, Hammer B (2024)
In: 2024 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-8.

A Water Futures Approach on Water Demand Forecasting with Online Ensemble Learning

Zanutto D, Michalopoulos C, Chatzistefanou G-A, Vamvakeridou-Lyroudia L, Tsiami L, Glynis K, Samartzis P, Hermes L, Hinder F, Vaquet J, Vaquet V, et al. (2024)
In: The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024). Basel Switzerland: MDPI: 60.

Physics-Informed Graph Neural Networks for Water Distribution Systems

Ashraf MI, Strotherm J, Hermes L, Hammer B (2024)
In: THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 20. Palo alto: Assoc Advancement Artificial Intelligence.

Feature-based analyses of concept drift

Hinder F, Vaquet V, Hammer B (2024)
Neurocomputing 600: 127968.

One or two things we know about concept drift-a survey on monitoring in evolving environments. Part A: detecting concept drift

Hinder F, Vaquet V, Hammer B (2024)
Frontiers in Artificial Intelligence 7: 1330257.

FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation

Velioglu R, Chan RK-W, Hammer B (2024)
arXiv:2404.08582.

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.

Predicting the Level of Co-Activation of One Muscle Head from the Other Muscle Head of the Biceps Brachii Muscle by Linear Regression and Shallow Feedforward Neural Networks

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.

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.

Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks

Vaquet V, Hinder F, Hammer B (2024)
In: Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods ICPRAM. Volume 1. SCITEPRESS - Science and Technology Publications: 296-303.

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.

For Better or Worse: The Impact of Counterfactual Explanations’ Directionality on User Behavior in xAI

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.

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). Advances in Neural Information Processing Systems. .

Metric Learning with Self-Adjusting Memory for Explaining Feature Drift

Kummert J, Schulz A, Hammer B (2023)
SN Computer Science 4(4): 376.

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.

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.

Let's go to the Alien Zoo: Introducing an experimental framework to study usability of counterfactual explanations for machine learning

Kuhl U, Artelt A, Hammer B (2023)
Frontiers in Computer Science 5: 1087929.

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.

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.

Robust Feature Selection and Robust Training to Cope with Hyperspectral Sensor Shifts

Vaquet V, Brinkrolf J, Hammer B (Accepted) .

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.

On the Change of Decision Boundary and Loss in Learning with Concept Drift

Hinder F, Vaquet V, Brinkrolf J, Hammer B (2023)
In: Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Crémilleux B, Hess S, Nijssen S (Eds); Lecture Notes in Computer Science, 13876. Cham: Springer : 182-194.

So Can We Use Intrinsic Bias Measures or Not?

Schroeder S, Schulz A, Kenneweg P, Hammer B (2023)
In: Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications: 403-410.

Debiasing Sentence Embedders Through Contrastive Word Pairs

Kenneweg P, Schroeder S, 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: 205-212.

"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.

On Feature Removal for Explainability in Dynamic Environments

Fumagalli F, Muschalik M, Hüllermeier E, Hammer B (2023)
In: ESANN 2023 proceedings. 83-88.

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.

Incremental permutation feature importance (iPFI): towards online explanations on data streams

Fumagalli F, Muschalik M, Hüllermeier E, Hammer B (2023)
Machine Learning .

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.

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.

Model-based explanations of concept drift

Hinder F, Vaquet V, Brinkrolf J, Hammer B (2023)
Neurocomputing: 126640.

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.

Feature Selection for Concept Drift Detection

Hinder F, Hammer B (2023)
In: ESANN 2023 Proceedings. Verleysen M (Ed); .

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.

Spatial Graph Convolution Neural Networks for Water Distribution Systems

Ashraf I, Hermes L, Artelt A, Hammer B (2023)
In: Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Crémilleux B, Hess S, Nijssen S (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 29-41.

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.

"I do not know! but why?"- Local model-agnostic example-based explanations of reject

Artelt A, Visser R, Hammer B (2023)
Neurocomputing 558: 126722.

Measuring Fairness with Biased Data: A Case Study on the Effects of Unsupervised Data in Fairness Evaluation

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.

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.

Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis

Stallmann D, Hammer B (2023)
Algorithms 16(4): 205.

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.

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.

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.

Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting

Kuhl U, Artelt A, Hammer B (2022)
In: 2022 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM: 2125-2137.

Stream-Based Active Learning with Verification Latency in Non-stationary Environments

Castellani A, Schmitt S, 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, 13532. Cham: Springer Nature Switzerland: 260-272.

“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.

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.

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.

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.

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.

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.

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.

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.

Agnostic Explanation of Model Change based on Feature Importance

Muschalik M, Fumagalli F, Hammer B, Hüllermeier E (2022)
KI - Künstliche Intelligenz.

Faire Algorithmen und die Fairness von Erklärungen: Informatische, rechtliche und ethische Perspektiven

Artelt A, Geminn C, Hammer B, Manzeschke A, Mavrina L, Weber C (2022)
DuEPublico: Duisburg-Essen Publications online, University of Duisburg-Essen, Germany.

Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data

Vaquet V, Menz P, Seiffert U, Hammer B (2022)
Neurocomputing.

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.

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 .

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.

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.

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.

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.

Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens

Hammer B, Hüllermeier E, Lohweg V, Schneider A, Schenck W, Kuhl U, Braun M, Pfeifer A, Holst C-A, Schmidt M, Schomaker G, et al. (2022)
Bielefeld: Univ. Bielefeld, Forschungsinstitut für Kognition und Robotik.

BERT WEAVER: Using WEight AVERaging to Enable Lifelong Learning for Transformer-based Models

Langnickel L, Schulz A, Hammer B, Fluck J (2022)
arXiv.

Interpretable locally adaptive nearest neighbors

Göpfert JP, Wersing H, Hammer B (2022)
Neurocomputing 470: 344-351.

Decentralized control and local information for robust and adaptive decentralized Deep Reinforcement Learning

Schilling M, Melnik A, Ohl FW, Ritter H, Hammer B (2021)
Neural Networks 144: 699-725.

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.

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.

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.

Estimating the Electrical Power Output of Industrial Devices with End-to-End Time-Series Classification in the Presence of Label Noise

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.

Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers

Artelt A, Hammer B (2021)
Neurocomputing 470(VSI: ESANN 2020): 304-317.

Contrastive Explanations for Explaining Model Adaptations

Artelt A, Hinder F, Vaquet V, Feldhans R, Hammer B (2021)
In: Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I. Rojas I, Joya G, Catala A (Eds); Lecture Notes in Computer Science. Cham: Springer : 101-112.

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.

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.

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.

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.

Können Kinder aufgeklärte Nutzer* innen von Sprachassistenten sein? Rechtliche, psychologische, ethische und informatische Perspektiven

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.

Efficient computation of contrastive explanations

Artelt A, Hammer B (2021)
In: 2021 International Joint Conference on Neural Networks (IJCNN). New York: Institute of Electrical and Electronics Engineers (IEEE): 1-9.

Explanation as a social practice: Toward a conceptual framework for the social design of AI systems

Rohlfing K, Cimiano P, Scharlau I, Matzner T, Buhl HM, Buschmeier H, Esposito E, Grimminger A, Hammer B, Häb-Umbach R, Horwath I, et al. (2021)
IEEE Transactions on Cognitive and Developmental Systems 13(3): 717--728.

Reservoir Stack Machines

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 .

Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting

Hermes L, Hammer B, Schilling M (2021)
In: ESANN 2021 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. . 111-116.

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.

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.

Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)

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.

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.

DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction

Schulz A, Hinder F, Hammer B (2020)
In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}.

Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework

Biehl M, Abadi F, Göpfert C, Hammer B (2020)
In: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. Proceedings of the 13th International Workshop, WSOM+ 2019, Barcelona, Spain, June 26-28, 2019. Vellido A, Gibert K, Angulo C, Martín Guerrero JD (Eds); Advances in Intelligent Systems and Computing. Cham: Springer International Publishing: 210-221.

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.

Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information

Pfannschmidt L, Jakob J, Hinder F, Biehl M, Tino P, Hammer B (2020)
Neurocomputing.

Sustainability and Trust for Artificial Intelligence Technologies

Hammer B, van der Aalst W, Bauckhage C, Behnke S, Holz T, Krämer N, Morik K, Ngonga Ngomo A-C (2020) .

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) .

On the computation of counterfactual explanations - A survey

Artelt A, Hammer B (2019)
arXiv: 1911.07749v1.

Recovering Localized Adversarial Attacks

Göpfert JP, Wersing H, Hammer B (2019)
In: Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I. Tetko IV, Kůrková V, Karpov P, Theis F (Eds); Lecture Notes in Computer Science. Cham: Springer International Publishing: 302-311.

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.

Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning

Hosseini B, Hammer B (2019)
In: 2019 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-8.

On the Identification of Decision Boundaries for Anomaly Detection in CPPS

Li P, Niggemann O, Hammer B (2019)
In: 2019 IEEE International Conference on Industrial Technology (ICIT). IEEE: 1311-1316.

Lecture Notes on Applied Optimization

Paaßen B, Artelt A, Hammer B (2019)
Faculty of Technology, Bielefeld University.

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.

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.

Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection

Hosseini B, Hammer B (Accepted)
Presented at the The 28th ACM International Conference on Information and Knowledge Management (CIKM) , Beijing.

KI-basierte Sprachassistenten im Alltag: Forschungsbedarf aus informatischer, psychologischer, ethischer und rechtlicher Sicht

Krämer N, Artelt A, Geminn C, Hammer B, Kopp S, Manzeschke A, Rossnagel A, Slawik P, Szczuka J, Varonina L, Weber C (2019)
Universität Duisburg-Essen, Universitätsbibliothek.

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.

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.

Differential privacy for learning vector quantization

Brinkrolf J, Göpfert C, Hammer B (2019)
Neurocomputing 342: 125-136.

Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series

Hosseini B, Hammer B (2019)
In: Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Verleysen M (Ed); .

Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning

Hosseini B, Hammer B (2019)
Presented at the The 2019 International Joint Conference on Neural Networks (IJCNN), Budapest.

Adversarial attacks hidden in plain sight

Göpfert JP, Wersing H, Hammer B (2019) .

Time integration and reject options for probabilistic output of pairwise LVQ

Brinkrolf J, Hammer B (2019)
Neural Computing and Applications.

Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto

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 .

Classification of motor errors to provide real-time feedback for sports coaching in virtual reality - A case study in squats and Tai Chi pushes (Data)

Hülsmann F, Göpfert JP, Hammer B, Kopp S, Botsch M (2018)
Bielefeld University.

Classification of motor errors to provide real-time feedback for sports coaching in virtual reality — A case study in squats and Tai Chi pushes

Hülsmann F, Göpfert JP, Hammer B, Kopp S, Botsch M (2018)
Computers & Graphics 76: 47-59.

Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto

Queisser JF, Hammer B, Ishihara H, Asada M, Steil JJ (2018)
In: 2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE: 39-45.

Non-negative Local Sparse Coding for Subspace Clustering

Hosseini B, Hammer B (2018)
In: Advances in Intelligent Data Analysis XVII. 17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24–26, 2018, Proceedings. Duivesteijn W, Siebes A, Ukkonen A (Eds); Lecture Notes in Computer Science. Cham: Springer International Publishing: 137-150.

Generation of Adversarial Examples to Prevent Misclassification of Deep Neural Network based Condition Monitoring Systems for Cyber-Physical Production Systems

Specht F, Otto J, Niggemann O, Hammer B (2018)
In: 2018 IEEE 16th International Conference on Industrial Informatics (INDIN). IEEE: 760-765.

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.

A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications

Li P, Niggemann O, Hammer B (2018)
In: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. IEEE: 5345-5352.

Statistical Mechanics of On-Line Learning Under Concept Drift

Straat M, Abadi F, Göpfert C, Hammer B, Biehl M (2018)
ENTROPY 20(10): 775.

flowLearn: Fast and precise identification and quality checking of cell populations in flow cytometry

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.

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.

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.

Label-noise-tolerant classification for streaming data

Frenay B, Hammer B (2017)
In: 2017 International Joint Conference on Neural Networks (IJCNN). IEEE: 1748-1755.

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.

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.

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.

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.

Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction

Göpfert JP, Göpfert C, Botsch M, Hammer B (2017)
In: 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE.

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.

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.

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.

Prototype-based Models for the Supervised Learning of Classification Schemes

Biehl M, Hammer B, Villmann T (2016)
Proceedings of the International Astronomical Union 12(S325): 129-138.

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.

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.

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.

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.

Adaptive structure metrics for automated feedback provision in intelligent tutoring systems

Paaßen B, Mokbel B, Hammer B (2016)
Neurocomputing 192(SI): 3-13.

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.

Odor recognition in robotics applications by discriminative time-series modeling

Schleif F-M, Hammer B, Gonzalez Monroy J, Gonzalez Jimenez J, Blanco-Claraco J-L, Biehl M, Petkov N (2016)
PATTERN ANALYSIS AND APPLICATIONS 19(1): 207-220.

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.

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.

Learning Feedback in Intelligent Tutoring Systems. Report of the FIT Project, Conducted from December 2011 to March 2015

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.

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.

Metric learning for sequences in relational LVQ

Mokbel B, Paaßen B, Schleif F-M, Hammer B (2015)
Neurocomputing 169(SI): 306-322.

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.

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.

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.

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).

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.

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.

Distance Measures for Prototype Based Classification

Biehl M, Hammer B, Villmann T (2014)
In: Brain-Inspired Computing. International Workshop, BrainComp 2013, Cetraro, Italy, July 8-11, 2013, Revised Selected Papers. Grandinetti L, Lippert T, Petkov N (Eds); Lecture Notes in Computer Science. Cham: Springer International Publishing: 100-116.

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.

Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches

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.

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.

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.

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.

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.

Sparse Prototype Representation by Core Sets

Schleif F-M, Zhu X, Hammer B (2013)
In: Intelligent Data Engineering and Automated Learning – IDEAL 2013. Yin H, Tang K, Gao Y, Klawonn F, Lee M, Weise T, Li B, Yao X (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 302-309.

Regularization and improved interpretation of linear data mappings and adaptive distance measures

Strickert M, Hammer B, Villmann T, Biehl M (2013)
In: 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE: 10-17.

Efficient Approximations of Kernel Robust Soft LVQ

Hofmann D, Gisbrecht A, Hammer B (2013)
In: Advances in Self-Organizing Maps. Estévez PA, Príncipe JC, Zegers P (Eds); Advances in Intelligent Systems and Computing. Berlin, Heidelberg: Springer Berlin Heidelberg: 183-192.

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.

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.

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.

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.

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.

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.

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.

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 basis-function t-SNE for fast nonlinear dimensionality reduction

Gisbrecht A, Mokbel B, Hammer B (2012)
In: The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-8.

Discriminative Dimensionality Reduction Mappings

Gisbrecht A, Hofmann D, Hammer B (2012)
In: Advances in Intelligent Data Analysis XI. Hollmén J, Klawonn F, Tucker A (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 126-138.

Kernel Robust Soft Learning Vector Quantization

Hofmann D, Hammer B (2012)
In: Artificial Neural Networks in Pattern Recognition. Mana N, Schwenker F, Trentin E (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 14-23.

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.

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.

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.

Special Issue on Neural Learning Paradigms

Hammer B (2012)
Künstliche Intelligenz :KI 26(4): 329-332.

Functional relevance learning in generalized learning vector quantization

Kaestner M, Hammer B, Biehl M, Villmann T (2012)
Neurocomputing 90: 85-95.

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.

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.

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.

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.

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.

Topographic Mapping of Dissimilarity Data

Hammer B, Gisbrecht A, Hasenfuss A, Mokbel B, Schleif F-M, Zhu X (2011)
In: Advances in Self-Organizing Maps. Laaksonen J, Honkela T (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 1-15.

Relational Extensions of Learning Vector Quantization

Hammer B, Schleif F-M, Zhu X (2011)
In: Neural Information Processing. Lu B-L, Zhang L, Kwok J (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 481-489.

Prototype-Based Classification of Dissimilarity Data

Hammer B, Mokbel B, Schleif F-M, Zhu X (2011)
In: Advances in Intelligent Data Analysis X. Gama J, Bradley E, Hollmén J (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 185-197.

Accelerating Kernel Neural Gas

Schleif F-M, Gisbrecht A, Hammer B (2011)
In: Artificial Neural Networks and Machine Learning – ICANN 2011. Honkela T, Duch W, Girolami M, Kaski S (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 150-158.

A General Framework for Dimensionality Reduction for Large Data Sets

Hammer B, Biehl M, Bunte K, Mokbel B (2011)
In: Advances in Self-Organizing Maps. Laaksonen J, Honkela T (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 277-287.

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.

Neighbor embedding XOM for dimension reduction and visualization

Bunte K, Hammer B, Villmann T, Biehl M, Wismueller A (2011)
Neurocomputing 74(9): 1340-1350.

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.

Patch Affinity Propagation

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.

Relevance learning in generative topographic mapping

Gisbrecht A, Hammer B (2011)
Neurocomputing 74(9): 1351-1358.

Visualizing Dissimilarity Data Using Generative Topographic Mapping

Gisbrecht A, Mokbel B, Hasenfuss A, Hammer B (2010)
In: KI 2010: Advances in Artificial Intelligence. Dillmann R, Beyerer J, Hanebeck UD, Schultz T (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 227-237.

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.

Global Coordination Based on Matrix Neural Gas for Dynamic Texture Synthesis

Arnonkijpanich B, Hammer B (2010)
In: Artificial Neural Networks in Pattern Recognition. 4th IAPR TC3 Workshop, ANNPR 2010, Cairo, Egypt, April 11-13, 2010. Proceedings. Schwenker F, El Gayar N (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 84-95.

Automated generation of classifier based monitoring functions and its application to automotive steering control

Haupt A, Wolf F, Bohn C, Hammer B (2010)
IFAC Proceedings Volumes 43(7): 721-726.

The Nystrom approximation for relational generative topographic mappings

Gisbrecht A, Mokbel B, Hammer B (2010)
In: NIPS workshop on challenges of Data Visualization.

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.

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.

Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization

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.

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.

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.

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.

Sparse representation of data

Villmann T, Schleif F-M, Hammer B (2010)
In: ESANN'10. Verleysen M (Ed); D side: 225-234.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Similarity Based Clustering

Biehl M, Hammer B, Verleysen M, Villmann T (Eds) (2009) Springer Lecture Notes Artificial Intelligence, 5400.
Berlin, Heidelberg: Springer.

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.

Pattern Recognition by Supervised Relevance Neural Gas and its Application to Spectral Data in Bioinformatics

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.

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).

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.

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.

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.

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.

Thinning Mesh Animations

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.

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.

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.

Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods

Villmann T, Schleif F-M, Kostrzewa M, Walch A, Hammer B (2008)
Briefings in Bioinformatics 9(2): 129-143.

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.

Relational topographic maps

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.

Aggregation of multiple peak lists by use of an improved neural gas network

Schleif F-M, Hasenfuss A, Hammer B (2007)
Leipzig: Universität Leipzig.

Dynamics and generalization ability of LVQ algorithms

Biehl M, Ghosh A, Hammer B (2007)
Journal of Machine Learning Research 8: 323-360.

Relational topographic Maps

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.

Relevance matrices in LVQ

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.

Margin based Active Learning for LVQ Networks

Schleif F-M, Hammer B, Villmann T (2007)
Neurocomputing 70(7-9): 1215-1224.

ICOLE-2007, German-Polish Workshop on Computational Biology, Scheduling and Machine Learning. Lessach, Austria, 27.05.-02.06.2007

Blazewicz J, Ecker K, Hammer B (2007)
Clausthal-Zellerfeld: Clausthal University of Technology.

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.

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.

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.

Relational Neural Gas

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.

Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties

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.

Supervised Neural Gas for Functional Data and its Application to the Analysis of Clinical Proteom Spectra

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.

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.

Supervised Batch Neural Gas

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 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.

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.

Batch and Median Neural Gas

Cottrell M, Hammer B, Hasenfuss A, Villmann T (2006)
Neural Networks 19(6-7): 762-771.

Supervised median neural gas

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.

Supervised median clustering

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.

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.

Comparison of relevance learning vector quantization with other metric adaptive classification methods

Villmann T, Schleif F-M, Hammer B (2006)
Neural Networks 19(5): 610-622.

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.

Supervised median clustering

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.

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 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.

Self Organizing Maps for Time Series

Hammer B, Micheli A, Neubauer N, Sperduti A, Strickert M (2005)
In: Proceedings of WSOM 2005. 115-122.

Merge SOM for temporal data

Strickert M, Hammer B (2005)
Neurocomputing 64: 39-71.

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.

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.

Batch NG

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.

Fuzzy Classification for Classification of Mass Spectrometric Data Based on Learning Vector Quantization

Villmann T, Schleif F-M, Hammer B (2005)
In: International Workshop on Integrative Bioinformatics.

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.

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.

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.

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.

Metrik Adaptation for Optimal Feature Classification in Learning Vector Quantization Applied to Environment Detection

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.

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.

Metric adaptation for optimal feature classification in learning vector quantization applied to environment detection

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.

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.

Relevance LVQ versus SVM

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.

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.

Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data

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.

On early stages of learning in connectionist models with feedback connections

Tino P, Hammer B (2004)
In: Compositional Connectionism in Cognitive Science.

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.

On the generalization ability of GRLVQ

Hammer B, Strickert M, Villmann T (2003) Osnabrücker Schriften zur Mathematik.
Osnabrück: Universität Osnabrück.

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.

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.

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.

Neural Gas for Sequences

Strickert M, Hammer B (2003)
In: WSOM'03. 53-57.

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.

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.

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.

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.

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.

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.

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.

Batch-GRLVQ

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.

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.

Closure properties of uniform convergence of empirical means and PAC learnability under a family of probability measures

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.

Closure properties of uniform convergence of empirical means and PAC learnability under a family of probability measures

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.

Generalization Ability of Folding Networks.

Hammer B (2001)
IEEE Trans. Knowl. Data Eng. 13(2): 196-206.

On the approximation capability of recurrent neural networks

Hammer B (2000)
Neurocomputing 31(1-4): 107-123.

Limitations of hybrid systems

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.

Barbara Hammer

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