PUB - Publikationen an der Universität Bielefeld

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• [8]
  

  2024 | Bielefelder E-Dissertation | PUB-ID: 2990589

  Jakob, J.: Incremental Learning in Regression Contexts. Universität Bielefeld, Bielefeld (2024).

  PUB | PDF
   
• [7]
  

  2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2979026

  Jakob, J., Artelt, A., Hasenjäger, M., Hammer, B.: Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams. Applied Artificial Intelligence. 37, : 2198846 (2023).

  PUB | DOI | WoS
   
• [6]
  

  2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982135

  Jakob, J., Hasenjäger, M., Hammer, B.: Reject Options for Incremental Regression Scenarios. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., and Aydin, M. (eds.) Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV. Lecture Notes in Computer Science. p. 248-259. Springer Nature Switzerland, Cham (2022).

  PUB | DOI
   
• [5]
  

  2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2969459

  Jakob, J., Artelt, A., Hasenjäger, M., Hammer, B.: SAM-kNN Regressor for Online Learning in Water Distribution Networks. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., and Aydin, M. (eds.) Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part III. Lecture Notes in Computer Science. 13531, p. 752-762. Springer Nature , Cham (2022).

  PUB | DOI
   
• [4]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982136

  Jakob, J., Hasenjäger, M., Hammer, B.: On the suitability of incremental learning for regression tasks in exoskeleton control. 2021 IEEE Symposium Series on Computational Intelligence (SSCI). p. 1-8. IEEE (2021).

  PUB | DOI
   
• [3]
  

  2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517

  Pfannschmidt, L., Jakob, J., Hinder, F., Biehl, M., Tino, P., Hammer, B.: Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing. (2020).

  PUB | DOI | Download (ext.) | WoS | arXiv
   
• [2]
  

  2019 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2935044

  Artelt, A., Jakob, J., Vaquet, V.: Continuous online user authentication based on keystroke dynamics. Presented at the Interdisciplinary College (IK), Günne/Möhnesee, Germany (2019).

  PUB | Dateien verfügbar
   
• [1]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893

  Pfannschmidt, L., Jakob, J., Biehl, M., Tino, P., Hammer, B.: Feature Relevance Bounds for Ordinal Regression. In: Verleysen, M. (ed.) Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). i6doc, Louvain-la-Neuve (2019).

  PUB | Download (ext.) | arXiv