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The Use of Bayesian Framework for Kernel Selection in Vector Machines Classifiers

  • Dmitry Kropotov
  • Nikita Ptashko
  • Dmitry Vetrov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

In the paper we propose a method based on Bayesian framework for selecting the best kernel function for supervised learning problem. The parameters of the kernel function are considered as model parameters and maximum evidence principle is applied for model selection. We describe a general scheme of Bayesian regularization, present model of kernel classifiers as well as our approximations for evidence estimation, and then give some results of experimental evaluation.

Keywords

Kernel Function Bayesian Framework Relevant Point Relevance Vector Machine Bayesian Regularization 
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.

References

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    Murphy, P.M., Aha, D.W.: UCI Repository of Machine Learning Databases [Machine Readable Data Repository]. Univ. of California, Dept. of Information and Computer Science, Irvine, Calif (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dmitry Kropotov
    • 1
  • Nikita Ptashko
    • 2
  • Dmitry Vetrov
    • 1
  1. 1.Dorodnicyn Computing CentreMoscowRussia
  2. 2.Moscow State UniversityMoscowRussia

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