Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/40432
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dc.contributor.authorNavarro, D.-
dc.contributor.authorLee, M.-
dc.date.issued2003-
dc.identifier.citationAdvances in neural information processing systems 15: proceedings of the 2002 conference / Suzanna Becker, Sebastian Thrun and Klaus Obermayer (eds.): pp.67-74-
dc.identifier.isbn0262025507-
dc.identifier.isbn9780262025508-
dc.identifier.issn1049-5258-
dc.identifier.urihttp://hdl.handle.net/2440/40432-
dc.description.abstractThis paper develops a new representational model of similarity data that combines continuous dimensions with discrete features. An algorithm capable of learning these representations is described, and a Bayesian model selection approach for choosing the appropriate number of dimensions and features is developed. The approach is demonstrated on a classic data set that considers the similarities between the numbers 0 through 9.-
dc.description.statementofresponsibilityDaniel J. Navarro; Michael D. Lee-
dc.description.urihttp://books.nips.cc/nips15.html-
dc.language.isoen-
dc.publisherMIT Press-
dc.relation.isreplacedby2440/90735-
dc.relation.isreplacedbyhttp://hdl.handle.net/2440/90735-
dc.source.urihttp://papers.nips.cc/paper/2249-combining-dimensions-and-features-in-similarity-based-representations-
dc.titleCombining dimensions and features in similarity-based representations-
dc.typeConference paper-
dc.contributor.conferenceNeural Information Processing Systems. Conference (16th : 2002 : British Columbia)-
dc.publisher.placeUnited States-
pubs.publication-statusPublished-
dc.identifier.orcidNavarro, D. [0000-0001-7648-6578]-
Appears in Collections:Aurora harvest 2
Environment Institute publications
Psychology publications

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