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dc.contributor.authorSchmidt, DF
dc.contributor.authorMakalic, E
dc.contributor.editorNicholson, A
dc.contributor.editorLi, X
dc.date.available2014-05-21T22:53:54Z
dc.date.issued2009-01-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000276823500032&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=d4d813f4571fa7d6246bdc0dfeca3a1c
dc.identifier.citationSchmidt, D. F. & Makalic, E. (2009). MML Invariant Linear Regression. AI 2009: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 5866, pp.312-315. https://doi.org/10.1007/978-3-642-10439-8_32.
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11343/29323
dc.languageEnglish
dc.publisherSPRINGER-VERLAG BERLIN
dc.subjectArtificial Intelligence and Image Processing
dc.titleMML Invariant Linear Regression
dc.typeJournal Article
dc.identifier.doi10.1007/978-3-642-10439-8_32
melbourne.peerreviewPeer Reviewed
melbourne.affiliationThe University of Melbourne
melbourne.affiliation.departmentPopulation Health
melbourne.source.titleAI 2009: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS
melbourne.source.volume5866
melbourne.source.pages312-315
dc.research.codefor801
dc.description.pagestart312
melbourne.publicationid141548
melbourne.elementsid320727
melbourne.contributor.authorSchmidt, Daniel
melbourne.contributor.authorMakalic, Enes
dc.identifier.eissn1611-3349
melbourne.conference.locationMelbourne, AUSTRALIA
melbourne.accessrightsThis item is currently not available from this repository


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