Statistical Learning Theory
This chapter presents techniques for statistical machine learning using Support Vector Machines (SVM) to recognize the patterns and classify them, predicting structured objects using SVM, k-nearest neighbor method for classification, and Naive Bayes classifiers.
Abstract
A machine learning system, in general, learns from the environment, but statistical machine learning programs (systems) learn from the data. This chapter presents techniques for statistical machine learning using Support Vector Machines (SVM) to recognize the patterns and classify them, predicting structured objects using SVM, k-nearest neighbor method for classification, and Naive Bayes classifiers. The artificial neural networks are presented with brief introduction to error-correction rules, Boltzmann learning, Hebbian rule, competitive learning rule, and deep learning. The instance-based learning is treated in details with its algorithm and learning task. The chapter concludes with a summary, and a set of practice exercises.