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Kernel Robust Soft Learning Vector Quantization

  • Daniela Hofmann
  • Barbara Hammer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7477)

Abstract

Prototype-based classification schemes offer very intuitive and flexible classifiers with the benefit of easy interpretability of the results and scalability of the model complexity. Recent prototype-based models such as robust soft learning vector quantization (RSLVQ) have the benefit of a solid mathematical foundation of the learning rule and decision boundaries in terms of probabilistic models and corresponding likelihood optimization. In its original form, they can be used for standard Euclidean vectors only. In this contribution, we extend RSLVQ towards a kernelized version which can be used for any positive semidefinite data matrix. We demonstrate the superior performance of the technique, kernel RSLVQ, in a variety of benchmarks where results competitive or even superior to state-of-the-art support vector machines are obtained.

Keywords

Support Vector Machine Learning Rule Vector Quantization Learn Vector Quantization Learn Vector Quantization Network 
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 2012

Authors and Affiliations

  • Daniela Hofmann
    • 1
  • Barbara Hammer
    • 1
  1. 1.CITEC Center of ExcellenceBielefeld UniversityGermany

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