An Elementary Introduction to Statistical Learning Theory

* Serves as a fundamental introduction to statistical learning theory and its role in understanding human learning and inductive reasoning. * Topics of coverage include: probability, pattern recognition, optimal Bayes decision rule, nearest neighbor rule, kernel rules, neural networks, and support vector machines.