PUB - Publikationen an der Universität Bielefeld

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• [13]
  

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

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

  PUB | PDF
   
• [12]
  

  2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2695196

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

  PUB | DOI | WoS
   
• [11]
  

  2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900320

  Frenay, B., et al., 2014. Valid interpretation of feature relevance for linear data mappings. In 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). Piscataway, NJ: Institute of Electrical & Electronics Engineers (IEEE), pp. 149-156.

  PUB | PDF | DOI
   
• [10]
  

  2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214

  Hofmann, D., et al., 2014. Learning interpretable kernelized prototype-based models. Neurocomputing, 141, p 84-96.

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

  2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2615730

  Hammer, B., et al., 2014. Learning vector quantization for (dis-)similarities. NeuroComputing, 131, p 43-51.

  PUB | DOI | WoS
   
• [8]
  

  2013 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982102

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

  PUB | DOI
   
• [7]
  

  2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625199

  Hofmann, D., & Hammer, B., 2013. Sparse approximations for kernel learning vector quantization. In ESANN.

  PUB
   
• [6]
  

  2012 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982106

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

  PUB | DOI
   
• [5]
  

  2012 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982107

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

  PUB | DOI
   
• [4]
  

  2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2671172

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

  PUB
   
• [3]
  

  2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625238

  Hofmann, D., Gisbrecht, A., & Hammer, B., 2012. Efficient Approximations of Kernel Robust Soft LVQ. In WSOM.

  PUB
   
• [2]
  

  2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625247

  Gisbrecht, A., Hofmann, D., & Hammer, B., 2012. Discriminative Dimensionality Reduction Mappings. In J. Hollmén, F. Klawonn, & A. Tucker, eds. Advances in Intelligent Data Analysis XI - 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012. Proceedings. Lecture Notes in Computer Science. no.7619 Springer, pp. 126-138.

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• [1]
  

  2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625254

  Hofmann, D., & Hammer, B., 2012. Kernel Robust Soft Learning Vector Quantization. In N. Mana, F. Schwenker, & E. Trentin, 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. no.7477 Springer, pp. 14-23.

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