
Contents
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Similarity-based models of categorization Similarity-based models of categorization
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Exemplars and prototypes Exemplars and prototypes
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Broader classes of representation Broader classes of representation
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Rational accounts of categorization Rational accounts of categorization
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The rational basis of exemplar and prototype models The rational basis of exemplar and prototype models
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The mixture model of categorization The mixture model of categorization
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Anderson's rational model of categorization Anderson's rational model of categorization
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Nonparametric Bayes and categorization Nonparametric Bayes and categorization
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Approximate inference algorithms Approximate inference algorithms
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The local MAP algorithm The local MAP algorithm
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Monte Carlo methods Monte Carlo methods
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Gibbs sampling Gibbs sampling
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Particle filtering Particle filtering
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Comparing the algorithms to data Comparing the algorithms to data
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Unifying rational models using hierarchical Dirichlet processes Unifying rational models using hierarchical Dirichlet processes
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Modeling the prototype-to-exemplar transition Modeling the prototype-to-exemplar transition
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Conclusion Conclusion
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Author note Author note
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References References
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14 Categorization as nonparametric Bayesian density estimation
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Published:March 2008
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Abstract
The authors apply the state of the art techniques from machine learning and statistics to reconceptualize the problem of unsupervised category learning, and to relate it to previous psychologically motivated models, especially Anderson's rational analysis of categorization. The resulting analysis provides a deeper understanding of the motivations underlying the classic models of category representation, based on prototypes or exemplars, as well as shedding new light on the empirical data. Exemplar models assume that a category is represented by a set of stored exemplars, and categorizing new stimuli involves comparing these stimuli to the set of exemplars in each category. Prototype models assume that a category is associated with a single prototype and categorization involves comparing new stimuli to these prototypes. These approaches to category learning correspond to different strategies for density estimation used in statistics, being nonparametric and parametric density estimation respectively.
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