PUB - Publikationen an der Universität Bielefeld

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• [11]
  

  2016 | Preprint | Veröffentlicht | PUB-ID: 2901439

  Fischer, L., & Villmann, T., 2016. A Probabilistic Model with Adaptive Rejection. Machine Learning Reports, MLR-01-2016:1-19.

  PUB
   
• [10]
  

  2016 | Konferenzbeitrag | PUB-ID: 2905195

  Fischer, L., Hammer, B., & Wersing, H., 2016. Online Metric Learning for an Adaptation to Confidence Drift. In Proceedings of International Joint Conference on Neural Networks (IJCNN). Vancouver: IEEE, pp. 748-755.

  PUB
   
• [9]
  

  2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2905193

  Fischer, L., Hammer, B., & Wersing, H., 2016. Optimal local rejection for classifiers. Neurocomputing, 214, p 445-457.

  PUB | DOI | WoS
   
• [8]
  

  2016 | Bielefelder E-Dissertation | PUB-ID: 2906747

  Fischer, L., 2016. Rejection and online learning with prototype-based classifiers in adaptive metrical spaces, Bielefeld: Universität Bielefeld.

  PUB | PDF
   
• [7]
  

  2015 | Preprint | PUB-ID: 2774656

  Fischer, L., Hammer, B., & Wersing, H., 2015. Optimum Reject Options for Prototype-based Classification.

  PUB | arXiv
   
• [6]
  

  2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774707

  Fischer, L., Hammer, B., & Wersing, H., 2015. Certainty-based Prototype Insertion/Deletion for Classification with Metric Adaptation. In ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 7-12.

  PUB
   
• [5]
  

  2015 | Konferenzbeitrag | PUB-ID: 2774721

  Fischer, L., Hammer, B., & Wersing, H., 2015. Combining Offline and Online Classifiers for Life-long Learning. In IJCNN, International Joint Conference on Neural Networks. pp. 2808-2815.

  PUB
   
• [4]
  

  2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772413

  Fischer, L., Hammer, B., & Wersing, H., 2015. Efficient rejection strategies for prototype-based classification. Neurocomputing, 169(SI), p 334-342.

  PUB | DOI | WoS
   
• [3]
  

  2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774643

  Fischer, L., et al., 2014. Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches. In T. Villmann, et al., eds. Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing. no.295 Cham: Springer International Publishing, pp. 109-118.

  PUB | DOI
   
• [2]
  

  2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673548

  Fischer, L., Hammer, B., & Wersing, H., 2014. Rejection strategies for learning vector quantization. In M. Verleysen, ed. ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium: i6doc.com, pp. 41-46.

  PUB
   
• [1]
  

  2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774498

  Fischer, L., Hammer, B., & Wersing, H., 2014. Local Rejection Strategies for Learning Vector Quantization. In S. Wermter, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2014. Lecture Notes in Computer Science. no.8681 Cham: Springer International Publishing, pp. 563-570.

  PUB | DOI