SVM regression through variational methods and its sequential implementation

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

    Research output: Contribution to journalArticlepeer-review

    24 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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