PUB - Publikationen an der Universität Bielefeld

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• [7]
  

  2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2979026

  Jakob, J., et al., 2023. Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams. Applied Artificial Intelligence, 37(1): 2198846.

  PUB | DOI | WoS
   
• [6]
  

  2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982135

  Jakob, J., Hasenjäger, M., & Hammer, B., 2022. Reject Options for Incremental Regression Scenarios. In E. Pimenidis, et al., 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. Cham: Springer Nature Switzerland, pp. 248-259.

  PUB | DOI
   
• [5]
  

  2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2969459

  Jakob, J., et al., 2022. SAM-kNN Regressor for Online Learning in Water Distribution Networks. In E. Pimenidis, et al., 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. no.13531 Cham: Springer Nature , pp. 752-762.

  PUB | DOI
   
• [4]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982136

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

  PUB | DOI
   
• [3]
  

  2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517

  Pfannschmidt, L., et al., 2020. Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing.

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

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

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

  PUB | Dateien verfügbar
   
• [1]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933893

  Pfannschmidt, L., et al., 2019. Feature Relevance Bounds for Ordinal Regression. In M. Verleysen, ed. Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Louvain-la-Neuve: i6doc.

  PUB | Download (ext.) | arXiv