Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/68541
標題: A Bayes empirical Bayes decision rule for classification
作者: Li, T.F.
Yen, T.C.
關鍵字: Bayes decision rule
classification
empirical Bayes decision rule
unsupervised learning
exponential reliability
learning automata
cooperative game
model
regression
parameter
期刊/報告no:: Communications in Statistics-Theory and Methods, Volume 34, Issue 5, Page(s) 1137-1149.
摘要: For classification, it is known that the Bayes decision rule is the best decision rule, which gives the minimum probability of misclassification. It is difficult to use the Bayes decision rule, since it contains unknown parameters from each class. In this study, a set of unidentied samples ( patterns) is used to establish an optimal classifier such that ( 1) it only contains the observations of unclassified samples (testing samples), ( 2) no other classifier is strictly better than our optimal classifier, and ( 3) when the number of unidentifed samples increases, the recognition rate of our classifier converges to the rate of the Bayes decision rule. A Monte Carlo simulation study is presented to demonstrate the favorable recognition rates obtained from our optimal classifier, which quickly converge to the highest rates obtained from the real Bayes decision rule, where the parameters in each class are known.
URI: http://hdl.handle.net/11455/68541
ISSN: 0361-0926
文章連結: http://dx.doi.org/10.1081/sta-200056853
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