UNSUPERVISED LEARNING OF PRIOR WEIGHTS FOR BAYES RULES IN PATTERN RECOGNITION

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 s…