PUB - Publikationen an der Universität Bielefeld

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• [11]
  

  2016 | Preprint | Veröffentlicht | PUB-ID: 2901439

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

  PUB
   
• [10]
  

  2016 | Konferenzbeitrag | PUB-ID: 2905195

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

  PUB
   
• [9]
  

  2016 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2905193

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

  PUB | DOI | WoS
   
• [8]
  

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

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

  PUB | PDF
   
• [7]
  

  2015 | Preprint | PUB-ID: 2774656

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

  PUB | arXiv
   
• [6]
  

  2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774707

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

  PUB
   
• [5]
  

  2015 | Konferenzbeitrag | PUB-ID: 2774721

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

  PUB
   
• [4]
  

  2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772413

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

  PUB | DOI | WoS
   
• [3]
  

  2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774643

  Fischer L, Nebel D, Villmann T, Hammer B, Wersing H. Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Deterministic Approaches. In: Villmann T, Schleif F-M, Kaden M, Lange M, eds. Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing. Vol 295. Cham: Springer International Publishing; 2014: 109-118.

  PUB | DOI
   
• [2]
  

  2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673548

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

  PUB
   
• [1]
  

  2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2774498

  Fischer L, Hammer B, Wersing H. Local Rejection Strategies for Learning Vector Quantization. In: Wermter S, Weber C, Duch W, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2014. Lecture Notes in Computer Science. Vol 8681. Cham: Springer International Publishing; 2014: 563-570.

  PUB | DOI