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https://hdl.handle.net/2440/40432
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DC Field | Value | Language |
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dc.contributor.author | Navarro, D. | - |
dc.contributor.author | Lee, M. | - |
dc.date.issued | 2003 | - |
dc.identifier.citation | Advances in neural information processing systems 15: proceedings of the 2002 conference / Suzanna Becker, Sebastian Thrun and Klaus Obermayer (eds.): pp.67-74 | - |
dc.identifier.isbn | 0262025507 | - |
dc.identifier.isbn | 9780262025508 | - |
dc.identifier.issn | 1049-5258 | - |
dc.identifier.uri | http://hdl.handle.net/2440/40432 | - |
dc.description.abstract | This 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.statementofresponsibility | Daniel J. Navarro; Michael D. Lee | - |
dc.description.uri | http://books.nips.cc/nips15.html | - |
dc.language.iso | en | - |
dc.publisher | MIT Press | - |
dc.relation.isreplacedby | 2440/90735 | - |
dc.relation.isreplacedby | http://hdl.handle.net/2440/90735 | - |
dc.source.uri | http://papers.nips.cc/paper/2249-combining-dimensions-and-features-in-similarity-based-representations | - |
dc.title | Combining dimensions and features in similarity-based representations | - |
dc.type | Conference paper | - |
dc.contributor.conference | Neural Information Processing Systems. Conference (16th : 2002 : British Columbia) | - |
dc.publisher.place | United States | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Navarro, D. [0000-0001-7648-6578] | - |
Appears in Collections: | Aurora harvest 2 Environment Institute publications Psychology publications |
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