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.

A thought-provoking look at statistical learning theory and itsrole in understanding human learning and inductivereasoningA joint endeavor from leading researchers in the fields ofphilosophy and electrical engineering, An ElementaryIntroduction to Statistical Learning Theory is a comprehensiveand accessible primer on the rapidly evolving fields of statisticalpattern recognition and statistical learning theory. Explainingthese areas at a level and in a way that is not often found inother books on the topic, the authors present the basic theorybehind contemporary machine learning and uniquely utilize itsfoundations as a framework for philosophical thinking aboutinductive inference.Promoting the fundamental goal of statistical learning, knowingwhat is achievable and what is not, this book demonstrates thevalue of a systematic methodology when used along with the neededtechniques for evaluating the performance of a learning system.First, an introduction to machine learning is presented thatincludes brief discussions of applications such as imagerecognition, speech recognition, medical diagnostics, andstatistical arbitrage. To enhance accessibility, two chapters onrelevant aspects of probability theory are provided. Subsequentchapters feature coverage of topics such as the pattern recognitionproblem, optimal Bayes decision rule, the nearest neighbor rulekernel rules, neural networks, support vector machines, andboosting.Appendices throughout the book explore the relationship betweenthe discussed material and related topics from mathematicsphilosophy, psychology, and statistics, drawing insightfulconnections between problems in these areas and statisticallearning theory. All chapters conclude with a summary section, aset of practice questions, and a reference sections that supplieshistorical notes and additional resources for further study.An Elementary Introduction to Statistical Learning Theoryis an excellent book for courses on statistical learning theorypattern recognition, and machine learning at theupper-undergraduate and graduate levels. It also serves as anintroductory reference for researchers and practitioners in thefields of engineering, computer science, philosophy, and cognitivescience that would like to further their knowledge of thetopic.