Introduction to Statistical Pattern Recognition

Abstract

Statistical pattern recognition is a term used to cover all stages of an investigation from problem formulation and data collection through to discrimination and classification, assessment of results and interpretation. This chapter introduces some of the basic concepts in classification and describes the key issues. It presents two complementary approaches to discrimination, namely a decision theory approach based on calculation of probability density functions and the use of Bayes theorem, and a discriminant function approach. Many different forms of discriminant function have been considered in the literature, varying in complexity from the linear discriminant function to multiparameter nonlinear functions such as the multilayer perceptron. Regression is an important part of statistical pattern recognition. Regression analysis is concerned with predicting the mean value of the response variable given measurements on the predictor variables and assumes a model of the form. Controlled Vocabulary Terms Bayes' theorem; regression analysis; statistical process control