Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/70907
標題: Bayes empirical Bayes approach to unsupervised learning of parameters in pattern recognition
作者: Li, T.F.
關鍵字: classification
empirical Bayes
pattern recognition
stochastic
approximation
cooperative game
automata
期刊/報告no:: Pattern Recognition, Volume 33, Issue 2, Page(s) 333-340.
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. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
URI: http://hdl.handle.net/11455/70907
ISSN: 0031-3203
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