AN ADAPTIVE PATTERN CLASSIFICATION SYSTEM.
TEXAS UNIV AT AUSTIN LABS FOR ELECTRONICS AND RELATED SCIENCE RESEARCH
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Adaptive pattern classification is the assignment of patterns to classes based on typical patterns or training samples which are used by the system to determine the decision procedure. An adaptive pattern classification system is described that does not require a prior knowledge of the probability density of the pattern vectors for each class, as do the classical statistical techniques. Any decision rule that consists of a discriminant function that is a linear combination of arbitrary scalar functions of the pattern vector may be chosen on the basis of a priori knowledge about the classes, engineering judgement, and economic considerations. The system optimizes itself by adjustment of the decision parameters according to a weighted mean-square-error performance criterion, using a multivariable search technique.