Density models are a popular tool for building classifiers. When using density models to build a classifier, one typically learns a separate density modelf or each class of interest. These density models are then combined to make a classifier through the use of Bayes’ rule utilizing the prior distribution over the classes. In this paper, we provide a discriminative method for choosing among alternative density models for each class to improve classification accuracy.

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In  Proceedings of Ninth International Workshop on Artificial Intelligence and Statistics

Publisher  The Society for Artificial Intelligence and Statistics
Copyright © 2003 by The Society for Artificial Intelligence and Statistics.

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