Pattern Recognition, 4th Edition

Chapter 2. Classifiers Based on Bayes Decision Theory

2.1. Introduction

This is the first chapter, out of three, dealing with the design of the classifier in a pattern recognition system. The approach to be followed builds upon probabilistic arguments stemming from the statistical nature of the generated features. As has already been pointed out in the introductory chapter, this is due to the statistical variation of the patterns as well as to the noise in the measuring sensors. Adopting this reasoning as our kickoff point, we will design classifiers that classify an unknown pattern in the most probable of the classes. Thus, our task now becomes that of defining what “most probable” means.

Given a classification task of M classes, ω¹, ω²,…, ...