Some Studies on Pattern Recognition with Nonsupervised Learning Procedures
Generally speaking, the pattern recognition techniques must perform two basic functions, that is, the process of characterizing a class of the common pattern of inputs that belong to the same class and the process of classifying any input as a member of one of several classes. Now, the process of classification is based on a procedure of decision making in which a set of sample patterns may be attributed to the corresponding class. Let a set of sample patterns belonging to the same class be characterized by the set of parameters accompanied by the probability distribution which governs how each pattern involved in the same class is generated. This is the method of Parametric Statistics. In the parametric methods the training set whose statistical structures are in some cases known correctly and in other cases unknown is used for the purpose of obtaining estimates of the parameter values on the basis of the Bayes’ estimation theory, and the discriminant function which may implement the process of classification is then determined by these estimates. This is the so-called Bayes’ Machine.
KeywordsPattern Recognition Random Environment Posteriori Probability Sample Pattern Pattern Recognition Technique
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