Pattern Recognition ( Third Edition )
نویسندگان
چکیده
Pattern recognition (PR) (or classification or discrimination or analysis) concerns the development of theoretical and computational means for placing abstract objects into categories. PR spans theoretical work involving techniques from probability and statistics, information theory, learning theory, and more [1, 2, 3, 4]. PR also plays a major role in applications in fields such as machine learning [5], data mining [6], and bioinformatics [7]. More generally, PR is a fundamental component in the ongoing quest for more intelligent machines. Modern computers have made it possible to collect and store enormous quantities of data. The automated analysis, classification, and retrieval of this data is of great importance and provides a major motivation for further developments in the field of PR. The book under review provides a comprehensive and self-contained introduction to PR. The first chapter introduces some basic terms such as features and supervised and unsupervised learning. A short discussion on the history of the field could have been a welcome addition. Ch. 2 provides the rudiments of Bayes decision theory, including a discussion on Bayesian networks that was added in the current edition. Ch. 3 describes classifiers that realize a linear decision surface, starting with the celebrated perceptron and ending with support vector machines (SVMs). Ch. 4 describes nonlinear classifiers with an emphasis on neural nets. The current edition also includes a discussion on architectures that combine several different classifiers. Ch. 5 deals with the problem of identifying a subset of the features that are the most relevant for the classification task. This includes
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