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. Proceedings of International Joint Conference on Neural Networks (IJCNN), 748-755

  PUB
   
• [9]
  

  2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2905193

  Fischer, L., Hammer, B., & Wersing, H. (2016). Optimal local rejection for classifiers. Neurocomputing, 214, 445-457. doi:10.1016/j.neucom.2016.06.038

  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. ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 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. IJCNN, International Joint Conference on Neural Networks, 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), 334-342. doi:10.1016/j.neucom.2014.10.092

  PUB | DOI | WoS
   
• [3]
  

  2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774643

  Fischer, L., Nebel, D., Villmann, T., Hammer, B., & Wersing, H. (2014). Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches. In T. Villmann, F. - M. Schleif, M. Kaden, & M. Lange (Eds.), Advances in Intelligent Systems and Computing: Vol. 295. Advances in Self-Organizing Maps and Learning Vector Quantization (pp. 109-118). Cham: Springer International Publishing. doi:10.1007/978-3-319-07695-9_10

  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 (pp. 41-46). Bruges, Belgium: i6doc.com.

  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, C. Weber, W. Duch, T. Honkela, P. Koprinkova-Hristova, S. Magg, G. Palm, et al. (Eds.), Lecture Notes in Computer Science: Vol. 8681. Artificial Neural Networks and Machine Learning – ICANN 2014 (pp. 563-570). Cham: Springer International Publishing. doi:10.1007/978-3-319-11179-7_71

  PUB | DOI