PUB - Publikationen an der Universität Bielefeld

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• [7]
  

  2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2979026

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

  PUB | DOI | WoS
   
• [6]
  

  2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982135

  J. Jakob, M. Hasenjäger, and B. Hammer, “Reject Options for Incremental Regression Scenarios”, Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV, E. Pimenidis, et al., eds., Lecture Notes in Computer Science, Cham: Springer Nature Switzerland, 2022, pp.248-259.

  PUB | DOI
   
• [5]
  

  2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2969459

  J. Jakob, et al., “SAM-kNN Regressor for Online Learning in Water Distribution Networks”, Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part III, E. Pimenidis, et al., eds., Lecture Notes in Computer Science, vol. 13531, Cham: Springer Nature , 2022, pp.752-762.

  PUB | DOI
   
• [4]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982136

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

  PUB | DOI
   
• [3]
  

  2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517

  L. Pfannschmidt, et al., “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

  A. Artelt, J. Jakob, and V. Vaquet, “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

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

  PUB | Download (ext.) | arXiv