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dc.contributor.authorNavarro, D.J.-
dc.contributor.editorLafferty, J.D.-
dc.contributor.editorWilliams, C.K.I.-
dc.contributor.editorShawe-Taylor, J.-
dc.contributor.editorZemel, R.S.-
dc.contributor.editorCulotta, A.-
dc.identifier.citationAdvances in Neural Information Processing Systems 23, 2010 / Lafferty, J.D., Williams, C.K.I., Shawe-Taylor, J., Zemel, R.S., Culotta, A. (ed./s), pp.1-9-
dc.description.abstractThis paper outlines a hierarchical Bayesian model for human category learning that learns both the organization of objects into categories, and the context in which this knowledge should be applied. The model is fit to multiple data sets, and provides a parsimonious method for describing how humans learn context specific conceptual representations.-
dc.description.statementofresponsibilityDaniel J. Navarro-
dc.publisherNeural Information Processing Systems Foundation-
dc.rights© The Author-
dc.titleLearning the context of a category-
dc.typeConference paper-
dc.contributor.conference24th Annual Conference on Neural Information Processing Systems 2010 (NIPS 2010) (6 Dec 2010 - 11 Dec 2010 : Vancouver, Canada)-
dc.identifier.orcidNavarro, D.J. [0000-0001-7648-6578]-
Appears in Collections:Aurora harvest 2
Psychology publications

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