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
CountryChina
Period30/10/0901/11/09

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