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Type: Conference paper
Title: Learning the context of a category
Author: Navarro, D.J.
Citation: Advances 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
Publisher: Neural Information Processing Systems Foundation
Issue Date: 2010
ISBN: 9781617823800
Conference Name: 24th Annual Conference on Neural Information Processing Systems 2010 (NIPS 2010) (6 Dec 2010 - 11 Dec 2010 : Vancouver, Canada)
Editor: Lafferty, J.D.
Williams, C.K.I.
Shawe-Taylor, J.
Zemel, R.S.
Culotta, A.
Statement of
Daniel J. Navarro
Abstract: This 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.
Rights: © The Author
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Appears in Collections:Aurora harvest 2
Psychology publications

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