Bo Thiesson and Christopher Meek
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.
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.