PUB - Publikationen an der Universität Bielefeld

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• [11]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982084

  Losing, V.; Yoshikawa, T.; Hasenjaeger, M.; Hammer, B.; Wersing, H. (2019): Personalized Online Learning of Whole-Body Motion Classes using Multiple Inertial Measurement Units. In: 2019 International Conference on Robotics and Automation (ICRA). IEEE. S. 9530-9536.

  PUB | DOI
   
• [10]
  

  2019 | Bielefelder E-Dissertation | PUB-ID: 2936581

  Losing, V. (2019): Memory Models for Incremental Learning Architectures. Bielefeld: Universität Bielefeld.

  PUB | PDF | DOI
   
• [9]
  

  2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982088

  Losing, V.; Wersing, H.; Hammer, B. (2018): Enhancing Very Fast Decision Trees with Local Split-Time Predictions. In: 2018 IEEE International Conference on Data Mining (ICDM). IEEE. S. 287-296.

  PUB | DOI
   
• [8]
  

  2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2917201

  Losing, V.; Hammer, B.; Wersing, H. (2018): Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM) KNOWLEDGE AND INFORMATION SYSTEMS,54:(1): 171-201.

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• [7]
  

  2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914730

  Losing, V.; Hammer, B.; Wersing, H. (2018): Incremental on-line learning: A review and comparison of state of the art algorithms Neurocomputing,275: 1261-1274.

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• [6]
  

  2017 | Konferenzbeitrag | PUB-ID: 2914734

  Losing, V.; Hammer, B.; Wersing, H. (2017): Self-Adjusting Memory: How to Deal with Diverse Drift Types. International Joint Conferences on Artificial Intelligence.

  PUB | PDF | DOI
   
• [5]
  

  2017 | Konferenzbeitrag | PUB-ID: 2914732

  Losing, V.; Hammer, B.; Wersing, H. (2017): Personalized Maneuver Prediction at Intersections.

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• [4]
  

  2016 | Konferenzbeitrag | PUB-ID: 2907624

  Losing, V.; Hammer, B.; Wersing, H. (2016): Choosing the Best Algorithm for an Incremental On-line Learning Task.

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• [3]
  

  2016 | Konferenzbeitrag | PUB-ID: 2908455

  Losing, V.; Hammer, B.; Wersing, H. (2016): Dedicated Memory Models for Continual Learning in the Presence of Concept Drift.

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• [2]
  

  2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2907622

  Losing, V.; Hammer, B.; Wersing, H. (2016): KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift. In: 2016 IEEE 16th International Conference on Data Mining (ICDM). Piscataway, NJ: IEEE. S. 291-300.

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• [1]
  

  2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2776021

  Losing, V.; Hammer, B.; Wersing, H. (2015): Interactive Online Learning for Obstacle Classification on a Mobile Robot. IEEE.

  PUB | PDF | DOI