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By Mendel

ISBN-10: 0124907504

ISBN-13: 9780124907508

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Additional info for Adaptive, Learning and Pattern Recognition Systems: Theory and Applications

Example text

Additions to the Classical Model T h e classical model of pattern recognition involves three major operations: representation, feature extraction, and classification. Though arbitrary and oversimplified, this model allows the formulation and discussion of many important problems, and provides a pleasant way of formalizing the classification problem. However, in particular applications this model may omit some of the most significant aspects of the problem. For example, in some situations it may be very expensive or time consuming to measure features.

S. Fu STATISTICAL. PATTERN RECOGNITION I, Statistical Pattern Recognition Systems and Bayes Classifiers A pattern recognition system, in general, consists of two parts, namely, feature extractor and classifier. * T h e function of feature extractor is to extract or to measure the important characteristics from the input patterns. T h e extracted characteristics are called features, and they are supposed to best characterize all the possible input patterns. Usually, if the cost of extracting features is not considered, the number of features characterizing input patterns can be arbitrarily large.

A set of sample patterns is partitioned into subsets by considering the patterns in sequence. T h e n the next pattern x2 is considered, and the distance 11 x2 - m, 11 is computed. If this distance is less than r , x2 is also assigned to the first subset, and m, is updated so that it is the average of x1 and x2. I n general, if n subsets have been created and a new pattern x is introduced, all n distances 11 x - mi I] are computed. If the smallest is less than Y , x is assigned to that subset and the corresponding mean vector is updated.

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Adaptive, Learning and Pattern Recognition Systems: Theory and Applications by Mendel

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