PUB - Publikationen an der Universität Bielefeld

Alle markieren

• [13]
  

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

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

  PUB | PDF
   
• [12]
  

  2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2695196

  D. Hofmann, A. Gisbrecht, and B. Hammer, “Efficient approximations of robust soft learning vector quantization for non-vectorial data”, Neurocomputing, vol. 147, 2015, pp. 96-106.

  PUB | DOI | WoS
   
• [11]
  

  2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900320

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

  PUB | PDF | DOI
   
• [10]
  

  2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214

  D. Hofmann, et al., “Learning interpretable kernelized prototype-based models”, Neurocomputing, vol. 141, 2014, pp. 84-96.

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

  2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2615730

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

  PUB | DOI | WoS
   
• [8]
  

  2013 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982102

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

  PUB | DOI
   
• [7]
  

  2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625199

  D. Hofmann and B. Hammer, “Sparse approximations for kernel learning vector quantization”, ESANN, 2013.

  PUB
   
• [6]
  

  2012 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982106

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

  PUB | DOI
   
• [5]
  

  2012 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982107

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

  PUB | DOI
   
• [4]
  

  2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2671172

  D. Hofmann, A. Gisbrecht, and B. Hammer, “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

  D. Hofmann, A. Gisbrecht, and B. Hammer, “Efficient Approximations of Kernel Robust Soft LVQ”, WSOM, 2012.

  PUB
   
• [2]
  

  2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625247

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

  PUB
   
• [1]
  

  2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625254

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

  PUB