Training the Classifiers

Contents

The model of a set of classifiers consists of the classifiers themselves and the mixing model. The classifiers are localised linear regression or classification models that are trained independently of each other, and their localisation is determined by the matching function m _k . This chapter is entirely devoted to the training of a single classifier and mainly focuses on the linear regression models, but also briefly discusses classification at the end of the chapter.

The linear classifier model was already introduced in Sec. 4.2.1, but here more details are provided about its underlying assumptions, and how it can be trained in both a batch learning and an incremental learning way. Most of the concepts and methods in this chapter are well known in statistics (for example, [97]) and adaptive filter theory (for example, [105]), but have not been put into the context of LCS before.