9Classifier-independent concepts

In the final chapter of this book, we will explore topics that are, in a sense, orthogonal to the classifiers we have seen so far. The first section deals with fundamental concepts and the limits of all statistical learning. The following sections will give an overview of methods for empirically evaluating of a classifier’s performance. The last two sections will introduce boosting, a meta-technique for combining the predictions of several weak classifiers into one strong classifier, and will discuss techniques for classifying with the option of rejecting a sample.

9.1Learning theory

So far we have gained an understanding of Bayesian classification that uses probability density estimates to derive a classification ...

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