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Learning Vector Quantization Classification with Local Relevance Determination for Medical Data

  • B. Hammer
  • T. Villmann
  • F. -M. Schleif
  • C. Albani
  • W. Hermann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)

Abstract

In this article we extend the global relevance learning vector quantization approach by local metric adaptation to obtain a locally optimized model for classification. In this sense we make a step in the direction of quadratic discriminance analysis in statistics where classwise variance matrices are used for class adapted discriminance functions. We demonstrateb the performance of the model for a medical application.

Keywords

Cost Function Generalization Ability Learn Vector Quantization Generalization Bound Generalize Learn Vector Quantization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • B. Hammer
    • 1
  • T. Villmann
    • 2
  • F. -M. Schleif
    • 3
  • C. Albani
    • 2
  • W. Hermann
    • 4
  1. 1.Institute of Computer ScienceClausthal University of TechnologyClausthal-ZellerfeldGermany
  2. 2.Clinic for PsychotherapyUniversity of LeipzigLeipzigGermany
  3. 3.Institute of Computer ScienceUniversity of LeipzigGermany
  4. 4.Paracelsus Hospital ZwickauGermany

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