8 Publikationen
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2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2979026Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data StreamsPUB | DOI | WoS
Jakob J, Artelt A, Hasenjäger M, Hammer B (2023)
Applied Artificial Intelligence 37(1): 2198846. -
2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982135Reject Options for Incremental Regression ScenariosPUB | DOI
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. -
2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2969459SAM-kNN Regressor for Online Learning in Water Distribution NetworksPUB | DOI
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. -
2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982136On the suitability of incremental learning for regression tasks in exoskeleton controlPUB | DOI
Jakob J, Hasenjäger M, Hammer B (2021)
In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE: 1-8. -
2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged InformationPUB | DOI | Download (ext.) | WoS | arXiv
Pfannschmidt L, Jakob J, Hinder F, Biehl M, Tino P, Hammer B (2020)
Neurocomputing. -
2019 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2935044Continuous online user authentication based on keystroke dynamicsPUB | Dateien verfügbar
Artelt A, Jakob J, Vaquet V (2019)
Presented at the Interdisciplinary College (IK), Günne/Möhnesee, Germany. -
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893Feature Relevance Bounds for Ordinal RegressionPUB | Download (ext.) | arXiv
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.