SVM regression through variational methods and its sequential implementation

Junbin Gao, S.R. Gunn, C.J. Harris

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

We consider an SVM regression model based on kernel methods with a Gaussian prior distribution over the network parameters. We show that the variational techniques can be utilised to obtain a closed form a posteriori distribution over the parameters given the data hence yielding an a posteriori predictive model.
Original languageEnglish
Pages (from-to)151-167
Number of pages17
JournalNeurocomputing
Volume55
Issue number1-2
DOIs
Publication statusPublished - 2003

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