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• [7]
  

  2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2979026

  Jakob, J., Artelt, A., Hasenjäger, M., & Hammer, B. (2023). Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams. Applied Artificial Intelligence, 37(1), 2198846. https://doi.org/10.1080/08839514.2023.2198846

  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, P. Angelov, C. Jayne, A. Papaleonidas, & M. Aydin (Eds.), Lecture Notes in Computer Science. Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV (pp. 248-259). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-15937-4_21

  PUB | DOI
   
• [5]
  

  2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2969459

  Jakob, J., Artelt, A., Hasenjäger, M., & Hammer, B. (2022). SAM-kNN Regressor for Online Learning in Water Distribution Networks. In E. Pimenidis, P. Angelov, C. Jayne, A. Papaleonidas, & M. Aydin (Eds.), Lecture Notes in Computer Science: Vol. 13531. Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part III (pp. 752-762). Cham: Springer Nature . https://doi.org/10.1007/978-3-031-15934-3_62

  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. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8. IEEE. https://doi.org/10.1109/SSCI50451.2021.9660138

  PUB | DOI
   
• [3]
  

  2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517

  Pfannschmidt, L., Jakob, J., Hinder, F., Biehl, M., Tino, P., & Hammer, B. (2020). Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing. doi:10.1016/j.neucom.2019.12.133

  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., Jakob, J., Biehl, M., Tino, P., & Hammer, B. (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