Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/44918
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dc.contributor.authorNavarro, D.en
dc.contributor.authorGriffiths, T.en
dc.date.issued2007en
dc.identifier.citationAdvances in Neural Information Processing Systems 19 (NIPS 2006), 2007 / Schölkopf, B., Platt, J., Hoffman, T. (ed./s), pp.1033-1040en
dc.identifier.isbn9780262195683en
dc.identifier.issn1049-5258en
dc.identifier.urihttp://hdl.handle.net/2440/44918-
dc.description.abstractThe additive clustering model is widely used to infer the features of a set of stimuli from their similarities, on the assumption that similarity is a weighted linear function of common features. This paper develops a fully Bayesian formulation of the additive clustering model, using methods from nonparametric Bayesian statistics to allow the number of features to vary. We use this to explore several approaches to parameter estimation, showing that the nonparametric Bayesian approach provides a straightforward way to obtain estimates of both the number of features used in producing similarity judgments and their importance.en
dc.description.statementofresponsibilityDaniel J. Navarro, Thomas L. Griffithsen
dc.language.isoenen
dc.publisherMorgan Kaufmann Publishers, Inc.en
dc.relation.isreplacedby2440/90744-
dc.relation.isreplacedbyhttp://hdl.handle.net/2440/90744-
dc.rights© Authorsen
dc.source.urihttp://papers.nips.cc/paper/3136-a-nonparametric-bayesian-method-for-inferring-features-from-similarity-judgmentsen
dc.titleA nonparametric bayesian method for inferring features from similarity judgmentsen
dc.typeConference paperen
dc.identifier.rmid0020076966en
dc.contributor.conferenceNeural Information Processing Systems 2006 (04 Dec 2006 - 07 Dec 2006 : Vancouver, Canada)en
dc.identifier.pubid44574-
pubs.library.collectionPsychology publicationsen
pubs.library.teamDS01en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidNavarro, D. [0000-0001-7648-6578]en
Appears in Collections:Psychology publications

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