Title: UNSUPERVISED LEARNING OF PRIOR WEIGHTS FOR BAYES RULES IN PATTERN RECOGNITION
Abstract: In the pattern classification problem, it is known that the Bayes decision rule, which separates k classes, gives a minimum probability of misclassification. In this study, the prior probability of each class is unknown and the conditional density functions are known. A set of unidentified input patterns is used to establish an empirical Bayes rule, which separates k classes and which leads to a stochastic approximation procedure for estimation of the priors. This classifier can adapt itself to a better decision rule by making use of unidentified input patterns while the system is in use. The resulting probability of misclassification can be made arbitrarily close to that of the Bayes rule. The results of a Monte Carlo simulation study with normal, uniform and exponential distributions are presented to demonstrate the favorable prior estimation for the empirical Bayes rule.
Publication Year: 1994
Publication Date: 1994-02-01
Language: en
Type: article
Indexed In: ['crossref']
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