Statistical Learning Theory

This chapter presents techniques for statistical machine learning, framing statistical systems as programs that learn from data rather than from the environment in general. It covers Support Vector Machines for recognizing and classifying patterns and for predicting structured objects, the k-nearest neighbor method for classification, and Naive Bayes classifiers.

The chapter also introduces artificial neural networks with brief treatments of error-correction rules, Boltzmann learning, the Hebbian rule, competitive learning, and deep learning, and treats instance-based learning in detail with its algorithm and learning task. It concludes with a summary and a set of practice exercises, providing an instructional overview of these core statistical learning methods.