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Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/4533
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dc.contributor.authorGao, Junbinen
dc.contributor.authorGunn, Steveen
dc.contributor.authorKandola, Jazen
local.source.editorEditor(s): Bob McKay and John Slaneyen
dc.date.accessioned2010-02-08T15:18:00Z-
dc.date.issued2002-
dc.identifier.citationAI 2002: Advances in Artificial Intelligence, p. 395-406en
dc.identifier.isbn9783540001973en
dc.identifier.isbn3540001972en
dc.identifier.urihttps://hdl.handle.net/1959.11/4533-
dc.description.abstractThis paper proposed a variational Bayesian approach for the SVM regression based on the likelihood model of an infinite mixture of Gaussians. To evaluate this approach the method was applied to synthetic datasets. We compared this new approximation approach with the standard SVM algorithm as well as other well established methods such as Gaussian Process.en
dc.languageenen
dc.publisherSpringeren
dc.relation.ispartofAI 2002: Advances in Artificial Intelligenceen
dc.relation.ispartofseriesLecture Notes in Computer Scienceen
dc.titleAdapting Kernels by Variational Approach in SVMen
dc.typeConference Publicationen
dc.relation.conference15th Australian Joint Conference on Artificial Intelligenceen
dc.identifier.doi10.1007/3-540-36187-1_35en
dc.subject.keywordsArtificial Intelligence and Image Processingen
local.contributor.firstnameJunbinen
local.contributor.firstnameSteveen
local.contributor.firstnameJazen
local.subject.for2008080199 Artificial Intelligence and Image Processing not elsewhere classifieden
local.subject.seo2008890205 Information Processing Services (incl. Data Entry and Capture)en
local.identifier.epublicationsvtls008681008en
local.profile.emailjgao@une.edu.auen
local.output.categoryE1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordpes:159en
local.date.conference2nd - 6th December, 2002en
local.conference.placeCanberra, Australiaen
local.publisher.placeBerlin, Germanyen
local.format.startpage395en
local.format.endpage406en
local.series.issn1611-3349en
local.series.issn0302-9743en
local.series.number2557en
local.peerreviewedYesen
local.contributor.lastnameGaoen
local.contributor.lastnameGunnen
local.contributor.lastnameKandolaen
local.seriespublisherSpringeren
local.seriespublisher.placeBerlin, Germanyen
dc.identifier.staffune-id:jgaoen
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:4642en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleAdapting Kernels by Variational Approach in SVMen
local.output.categorydescriptionE1 Refereed Scholarly Conference Publicationen
local.relation.urlhttp://trove.nla.gov.au/work/17085154en
local.conference.details15th Australian Joint Conference on Artificial Intelligence, Canberra, Australia, 2nd - 6th December, 2002en
local.search.authorGao, Junbinen
local.search.authorGunn, Steveen
local.search.authorKandola, Jazen
local.uneassociationUnknownen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2002en
local.date.start2002-12-02-
local.date.end2002-12-06-
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