PUB - Publikationen an der Universität Bielefeld

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• [8]
  

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

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

  PUB | PDF
   
• [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.

  PUB | DOI | WoS
   
• [6]
  

  2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982135

  Jakob J, Hasenjäger M, Hammer B (2022)
  Reject Options for Incremental Regression Scenarios.
  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.

  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: 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.

  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: 1-8.

  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.

  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: Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Verleysen M (Ed); Louvain-la-Neuve: i6doc.

  PUB | Download (ext.) | arXiv