Mean field method for the support vector machine regression

This paper deals with two subjects. First, we will show how support vector machine (SVM) regression problem can be solved as the maximum a posteriori prediction in the Bayesian framework. The second part describes an approximation technique that is useful in performing calculations for SVMs based on...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 50; pp. 391 - 405
Main Authors Gao, J.B., Gunn, S.R., Harris, C.J.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2003
Subjects
Gaussian process
Mean field method
Regression
Support vector machine
Regression
Gaussian process
Support vector machine
Mean field method
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Summary:This paper deals with two subjects. First, we will show how support vector machine (SVM) regression problem can be solved as the maximum a posteriori prediction in the Bayesian framework. The second part describes an approximation technique that is useful in performing calculations for SVMs based on the mean field algorithm which was originally proposed in Statistical Physics of disordered systems. One advantage is that it handle posterior averages for Gaussian process which are not analytically tractable.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0925-2312
1872-8286
DOI:10.1016/S0925-2312(02)00573-8