Abstract
Statistical pattern recognition is concerned with the problem of designing machines that can detect and classify complex patterns in data. Statistical pattern recognition problems frequently arise in all fields of science and engineering. In particular, statistical pattern recognition methods are widely used by social and behavioral scientists in data analysis and in the course of modeling complex social, behavioral, and neural systems. This article reviews the major components of statistical pattern recognition systems and discusses methods for classification and learning. In addition, the importance of evaluating generalization performance using cross-validation measures and other out-of-sample measures is emphasized.
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