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Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/2480
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dc.contributor.authorGao, Junbinen
dc.contributor.authorAntolovich, Michaelen
dc.contributor.authorKwan, Paul Hingen
local.source.editorEditor(s): W. Wobcke and M. Zhangen
dc.date.accessioned2009-10-13T15:52:00Z-
dc.date.issued2008-
dc.identifier.citationAI 2008: advances in artificial intelligence : 21st Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand, December 1-5, 2008, p. 318-324en
dc.identifier.isbn978-3-540-89377-6en
dc.identifier.urihttps://hdl.handle.net/1959.11/2480-
dc.description.abstractA new iterative procedure for solving regression problems with the so-called LASSO penalty is proposed by using generative Bayesian modeling and inference. The algorithm produces the anticipated parsimonious or sparse regression models that generalize well on unseen data. The proposed algorithm is quite robust and there is no need to specify any model hyperparameters. A comparison with state-of-the-art methods for constructing sparse regression models such as the relevance vector machine (RVM) and the local regularization assisted orthogonal least squares regression (LROLS) is given.en
dc.languageenen
dc.publisherSpringeren
dc.relation.ispartofAI 2008: advances in artificial intelligence : 21st Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand, December 1-5, 2008en
dc.relation.ispartofseriesLecture notes in artificial intelligenceen
dc.relation.ispartofseriesLecture notes in computer scienceen
dc.relation.isversionof1en
dc.titleL1 LASSO and its Bayesian Inferenceen
dc.typeConference Publicationen
dc.relation.conferenceAI 2008: 21st Australasian Joint Conference on Artificial Intelligenceen
dc.subject.keywordsPattern Recognition and Data Miningen
local.contributor.firstnameJunbinen
local.contributor.firstnameMichaelen
local.contributor.firstnamePaul Hingen
local.subject.for2008080109 Pattern Recognition and Data Miningen
local.subject.seo2008890299 Computer Software and Services not elsewhere classifieden
local.profile.schoolSchool of Science and Technologyen
local.profile.emailjgao@une.edu.auen
local.profile.emailwkwan2@une.edu.auen
local.output.categoryE1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordpes:6773en
local.date.conference1st - 5th December, 2008en
local.conference.placeAuckland, New Zealanden
local.publisher.placeBerlin, Germanyen
local.format.startpage318en
local.format.endpage324en
local.series.number5360en
local.contributor.lastnameGaoen
local.contributor.lastnameAntolovichen
local.contributor.lastnameKwanen
dc.identifier.staffune-id:jgaoen
dc.identifier.staffune-id:wkwan2en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:2553en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleL1 LASSO and its Bayesian Inferenceen
local.output.categorydescriptionE1 Refereed Scholarly Conference Publicationen
local.relation.urlhttp://nla.gov.au/anbd.bib-an44000781en
local.conference.detailsAI 2008: 21st Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand, December 1-5, 2008en
local.search.authorGao, Junbinen
local.search.authorAntolovich, Michaelen
local.search.authorKwan, Paul Hingen
local.uneassociationUnknownen
local.year.published2008en
local.date.start2008-12-01-
local.date.end2008-12-05-
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