Incremental Learning in Regression Contexts

Jakob J (2024)
Bielefeld: Universität Bielefeld.

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.

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.

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.

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.

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.

Continuous online user authentication based on keystroke dynamics

Artelt A, Jakob J, Vaquet V (2019)
Presented at the Interdisciplinary College (IK), Günne/Möhnesee, Germany.

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.