Mean Field Method for the Support Vector Machine Regression

Junbin Gao, Steve Gunn, Chris Harris

    Research output: Contribution to journalArticlepeer-review

    34 Citations (Scopus)
    125 Downloads (Pure)

    Abstract

    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.
    Original languageEnglish
    Pages (from-to)391-405
    Number of pages15
    JournalNeurocomputing
    Volume50
    Issue numberJanuary
    DOIs
    Publication statusPublished - 2003

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