PUB - Publikationen an der Universität Bielefeld

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• [13]
  

  2016 | Bielefelder E-Dissertation | PUB-ID: 2902065

  Hofmann, D.: Learning vector quantization for proximity data. Universität Bielefeld, Bielefeld (2016).

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• [12]
  

  2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2695196

  Hofmann, D., Gisbrecht, A., Hammer, B.: Efficient approximations of robust soft learning vector quantization for non-vectorial data. Neurocomputing. 147, 96-106 (2015).

  PUB | DOI | WoS
   
• [11]
  

  2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900320

  Frenay, B., Hofmann, D., Schulz, A., Biehl, M., Hammer, B.: Valid interpretation of feature relevance for linear data mappings. 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). p. 149-156. Institute of Electrical & Electronics Engineers (IEEE), Piscataway, NJ (2014).

  PUB | PDF | DOI
   
• [10]
  

  2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214

  Hofmann, D., Schleif, F.-M., Paaßen, B., Hammer, B.: Learning interpretable kernelized prototype-based models. Neurocomputing. 141, 84-96 (2014).

  PUB | DOI | Download (ext.) | WoS
   
• [9]
  

  2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2615730

  Hammer, B., Hofmann, D., Schleif, F.-M., Zhu, X.: Learning vector quantization for (dis-)similarities. NeuroComputing. 131, 43-51 (2014).

  PUB | DOI | WoS
   
• [8]
  

  2013 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982102

  Hofmann, D., Gisbrecht, A., Hammer, B.: Efficient Approximations of Kernel Robust Soft LVQ. In: Estévez, P.A., Príncipe, J.C., and Zegers, P. (eds.) Advances in Self-Organizing Maps. Advances in Intelligent Systems and Computing. p. 183-192. Springer Berlin Heidelberg, Berlin, Heidelberg (2013).

  PUB | DOI
   
• [7]
  

  2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625199

  Hofmann, D., Hammer, B.: Sparse approximations for kernel learning vector quantization. ESANN. (2013).

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• [6]
  

  2012 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982106

  Gisbrecht, A., Hofmann, D., Hammer, B.: Discriminative Dimensionality Reduction Mappings. In: Hollmén, J., Klawonn, F., and Tucker, A. (eds.) Advances in Intelligent Data Analysis XI. Lecture Notes in Computer Science. p. 126-138. Springer Berlin Heidelberg, Berlin, Heidelberg (2012).

  PUB | DOI
   
• [5]
  

  2012 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982107

  Hofmann, D., Hammer, B.: Kernel Robust Soft Learning Vector Quantization. In: Mana, N., Schwenker, F., and Trentin, E. (eds.) Artificial Neural Networks in Pattern Recognition. Lecture Notes in Computer Science. p. 14-23. Springer Berlin Heidelberg, Berlin, Heidelberg (2012).

  PUB | DOI
   
• [4]
  

  2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2671172

  Hofmann, D., Gisbrecht, A., Hammer, B.: Discriminative probabilistic prototype based models in kernel space. Workshop NC^2 2012. TR Machine Learning Reports (2012).

  PUB
   
• [3]
  

  2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625238

  Hofmann, D., Gisbrecht, A., Hammer, B.: Efficient Approximations of Kernel Robust Soft LVQ. WSOM. (2012).

  PUB
   
• [2]
  

  2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625247

  Gisbrecht, A., Hofmann, D., Hammer, B.: Discriminative Dimensionality Reduction Mappings. In: Hollmén, J., Klawonn, F., and Tucker, A. (eds.) Advances in Intelligent Data Analysis XI - 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings. Lecture Notes in Computer Science. 7619, p. 126-138. Springer (2012).

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• [1]
  

  2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625254

  Hofmann, D., Hammer, B.: Kernel Robust Soft Learning Vector Quantization. In: Mana, N., Schwenker, F., and Trentin, E. (eds.) Artificial Neural Networks in Pattern Recognition - 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 17-19, 2012. Proceedings. Lecture Notes in Computer Science. 7477, p. 14-23. Springer (2012).

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