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)


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.


Kernel Function Bayesian Framework Relevant Point Relevance Vector Machine Bayesian Regularization 
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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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