Bayes empirical Bayes approach to unsupervised learning of parameters in pattern recognition

摘要

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, all parameters in each class are unknown. 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 unknown parameters. This classifier can adapt itself to a better decision rule by making use of unidentified input patterns while the system is in use. The results of a Monte Carlo simulation study with normal distributions are presented to demonstrate the favorable estimation of unknown parameters for the empirical Bayes rule. The percentages of correct classification is also estimated by the Monte Carlo simulation.