Relevance Units Machine Based on Akaike's Information Criterion

Jun Zhang, Junbin Gao, Jinwen Tian

    Research output: Book chapter/Published conference paperConference paperpeer-review

    1 Citation (Scopus)

    Abstract

    The relevance vector machine (RVM) is a sparse regression kernel model. It not only generates a much sparser model but provides better generalization performance than the standard support vector machine (SVM). Relevance vectors and support vectors are both selected from the input vector set. This may limit model flexibility. Recently, we propose Relevance Units Machine (RUM). RUM treats relevance units (RUs) as part of the parameters of the model. However, the number of RUs must be selected before using RUM. In this paper, we use Akaike's Information Criterion (AIC) to select the number of the RUs. The experiment results show that based on AIC RUM maintains all the advantages of RVM and offers superior sparsity.
    Original languageEnglish
    Title of host publicationMIPPR 2009
    Subtitle of host publicationPattern Recognition and Computer Vision , Sixth International Symposium on Multispectral Image Processing and Pattern Recognition
    EditorsEds J Roberts
    Place of PublicationUSA
    PublisherSPIE
    Pages749624
    Number of pages1
    Volume7496
    DOIs
    Publication statusPublished - 2009
    EventInternational Symposium on Multispectral Image Processing and Pattern Recognition - Yichang, China, China
    Duration: 30 Oct 200901 Nov 2009

    Conference

    ConferenceInternational Symposium on Multispectral Image Processing and Pattern Recognition
    Country/TerritoryChina
    Period30/10/0901/11/09

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