Unifying rational models of categorization via the hierarchical Dirichlet process
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(Published version)
Date
2007
Authors
Griffiths, T.
Canini, K.
Sanborn, A.
Navarro, D.
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Kevin Gluck,
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Conference paper
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Proceedings of the 29th Annual Cognitive Science Society, August 1-4 2007, Nashville, Tennessee, pp.323-328.
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Thomas Griffiths, Kevin Canini, Adam Sanborn, Dan Navarro
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Annual Conference of the Cognitive Science Society (29th : 2007 : Nashville, Tennessee, USA)
Abstract
Models of categorization make different representational assumptions, with categories being represented by prototypes, sets of exemplars, and everything in between. Rational models of categorization justify these representational assumptions in terms of different schemes for estimating probability distributions. However, they do not answer the question of which scheme should be used in representing a given category. We show that existing rational models of categorization are special cases of a statistical model called the hierarchical Dirichlet process, which can be used to automatically infer a representation of the appropriate complexity for a given category.
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© the authors