Page 328 - Computational Statistics Handbook with MATLAB

Chapter 9

Statistical Pattern Recognition

9.1 Introduction
Statistical pattern recognition is an application in computational statistics
that uses many of the concepts we have covered so far, such as probability
density estimation and cross-validation. Examples where statistical pattern
recognition techniques can be used are numerous and arise in disciplines
such as medicine, computer vision, robotics, military systems, manufactur-
ing, finance and many others. Some of these include the following:

• A doctor diagnoses a patient’s illness based on the symptoms and
test results.
• A radiologist locates areas where there is non-healthy tissue in x-
rays.
• A military analyst classifies regions of an image as natural or man-
made for use in targeting systems.
• A geologist determines whether a seismic signal represents an
impending earthquake.
• A loan manager at a bank must decide whether a customer is a
good credit risk based on their income, past credit history and other
variables.
• A manufacturer must classify the quality of materials before using
them in their products.

In all of these applications, the human is often assisted by statistical pattern
recognition techniques.
Statistical methods for pattern recognition are covered in this chapter. In
this section, we first provide a brief introduction to the goals of pattern rec-
ognition and a broad overview of the main steps of building classifiers. In
Section 9.2 we present a discussion of Bayes classifiers and pattern recogni-
tion in an hypothesis testing framework. Section 9.3 contains techniques for

© 2002 by Chapman & Hall/CRC