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

Kropotov, Dmitry; Ptashko, Nikita; Vetrov, Dmitry

doi:10.1007/11578079_27

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

Dmitry Kropotov¹⁸,
Nikita Ptashko¹⁹ &
Dmitry Vetrov¹⁸

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Part of the Lecture Notes in Computer Science book series (LNIP,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.

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References

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Authors and Affiliations

Dorodnicyn Computing Centre, Vavilova str. 40, Moscow, 119991, Russia
Dmitry Kropotov & Dmitry Vetrov
Moscow State University, Vorob’evy gory, Moscow, 119234, Russia
Nikita Ptashko

Authors

Dmitry Kropotov
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Nikita Ptashko
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Dmitry Vetrov
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Editor information

Editors and Affiliations

Dept. System Engineering and Automation, Universitat Politècnica de Catalunya (UPC) Barcelona, Spain
Alberto Sanfeliu
Pattern Recognition Group, ICIMAF, Havana, Cuba
Manuel Lazo Cortés

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Kropotov, D., Ptashko, N., Vetrov, D. (2005). The Use of Bayesian Framework for Kernel Selection in Vector Machines Classifiers. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_27

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DOI: https://doi.org/10.1007/11578079_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
eBook Packages: Computer ScienceComputer Science (R0)

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