Lydia Fischer

lydia.fischer@uni-bielefeld.de
PEVZ-ID
45079367

11 Publikationen

Alle markieren

  • [11]
    2016 | Preprint | Veröffentlicht | PUB-ID: 2901439
    L. Fischer and T. Villmann, “A Probabilistic Model with Adaptive Rejection”, Machine Learning Reports, MLR-01-2016:1-19, 2016.
    PUB
     
  • [10]
    2016 | Konferenzbeitrag | PUB-ID: 2905195
    L. Fischer, B. Hammer, and H. Wersing, “Online Metric Learning for an Adaptation to Confidence Drift”, Proceedings of International Joint Conference on Neural Networks (IJCNN), Vancouver: IEEE, 2016, pp.748-755.
    PUB
     
  • [9]
    2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2905193
    L. Fischer, B. Hammer, and H. Wersing, “Optimal local rejection for classifiers”, Neurocomputing, vol. 214, 2016, pp. 445-457.
    PUB | DOI | WoS
     
  • [8]
    2016 | Bielefelder E-Dissertation | PUB-ID: 2906747 OA
    L. Fischer, Rejection and online learning with prototype-based classifiers in adaptive metrical spaces, Bielefeld: Universität Bielefeld, 2016.
    PUB | PDF
     
  • [7]
    2015 | Preprint | PUB-ID: 2774656
    L. Fischer, B. Hammer, and H. Wersing, “Optimum Reject Options for Prototype-based Classification”, 2015.
    PUB | arXiv
     
  • [6]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774707
    L. Fischer, B. Hammer, and H. Wersing, “Certainty-based Prototype Insertion/Deletion for Classification with Metric Adaptation”, ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2015, pp.7-12.
    PUB
     
  • [5]
    2015 | Konferenzbeitrag | PUB-ID: 2774721
    L. Fischer, B. Hammer, and H. Wersing, “Combining Offline and Online Classifiers for Life-long Learning”, IJCNN, International Joint Conference on Neural Networks, 2015, pp.2808-2815.
    PUB
     
  • [4]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772413
    L. Fischer, B. Hammer, and H. Wersing, “Efficient rejection strategies for prototype-based classification”, Neurocomputing, vol. 169, 2015, pp. 334-342.
    PUB | DOI | WoS
     
  • [3]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774643
    L. Fischer, et al., “Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches”, Advances in Self-Organizing Maps and Learning Vector Quantization, T. Villmann, et al., eds., Advances in Intelligent Systems and Computing, vol. 295, Cham: Springer International Publishing, 2014, pp.109-118.
    PUB | DOI
     
  • [2]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673548
    L. Fischer, B. Hammer, and H. Wersing, “Rejection strategies for learning vector quantization”, ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Bruges, Belgium: i6doc.com, 2014, pp.41-46.
    PUB
     
  • [1]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774498
    L. Fischer, B. Hammer, and H. Wersing, “Local Rejection Strategies for Learning Vector Quantization”, Artificial Neural Networks and Machine Learning – ICANN 2014, S. Wermter, et al., eds., Lecture Notes in Computer Science, vol. 8681, Cham: Springer International Publishing, 2014, pp.563-570.
    PUB | DOI
     
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