Overview

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 graduate 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.

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<p> 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. ( Zentralblatt MATH , 2012)<p>

The main focus of the book is on the ideas behind basic principles of learning theory and I can strongly recommend the book to anyone who wants to comprehend these ideas. ( Mathematical Reviews , 1 January 2013) 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. ( Zentralblatt MATH , 2012)

<p> The main focus of the book is on the ideas behind basicprinciples of learning theory and I can strongly recommend the bookto anyone who wants to comprehend these ideas. (Mathematical Reviews, 1 January 2013) <p> It also serves as an introductory reference forresearchers and practitioners in the fields of engineering,computer science, philosophy, and cognitive science that would liketo further their knowledge of the topic. (Zentralblatt MATH, 2012) <p>

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. ( Zentralblatt MATH , 2012)

Author Information

SANJEEV KULKARNI, PhD, is Professor in the Department of Electrical Engineering at Princeton University, where he is also an affiliated faculty member in the Department of Operations Research and Financial Engineering and the Department of Philosophy. Dr. Kulkarni has published widely on statistical pattern recognition, nonparametric estimation, machine learning, information theory, and other areas. A Fellow of the IEEE, he was awarded Princeton University's President's Award for Distinguished Teaching in 2007. GILBERT HARMAN, PhD, is James S. McDonnell Distinguished University Professor in the Department of Philosophy at Princeton University. A Fellow of the Cognitive Science Society, he is the author of more than fifty published articles in his areas of research interest, which include ethics, statistical learning theory, psychology of reasoning, and logic.

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