Pattern Recognition, 4th Edition

Chapter 3. Linear Classifiers

3.1. Introduction

Our major concern in Chapter 2 was to design classifiers based on probability density or probability functions. In some cases, we saw that the resulting classifiers were equivalent to a set of linear discriminant functions. In this chapter, we will focus on the design of linear classifiers, regardless of the underlying distributions describing the training data. The major advantage of linear classifiers is their simplicity and computational attractiveness. The chapter starts with the assumption that all feature vectors from the available classes can be classified correctly using a linear classifier, and we will develop techniques for the computation of the corresponding linear functions. In the ...