**A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning**

A joint endeavor from leading researchers in the fields of
philosophy and electrical engineering, *An Elementary
Introduction to Statistical Learning Theory* is a comprehensive
and accessible primer on the rapidly evolving fields of statistical
pattern recognition and statistical learning theory. Explaining
these areas at a level and in a way that is not often found in
other books on the topic, the authors present the basic theory
behind contemporary machine learning and uniquely utilize its
foundations as a framework for philosophical thinking about
inductive inference.

Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting.

Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study.

*An Elementary Introduction to Statistical Learning Theory*
is an excellent book for courses on statistical learning theory,
pattern recognition, and machine learning at the
upper-undergraduate and graduateĀ levels. It also serves as an
introductory reference for researchers and practitioners in the
fields of engineering, computer science, philosophy, and cognitive
science that would like to further their knowledge of the
topic.