An Elementary Introduction to Statistical Learning Theory

Beschreibung

A thought-provoking look at statistical learning theory and itsrole in understanding human learning and inductivereasoning A 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 rule,kernel rules, neural networks, support vector machines, andboosting. Appendices throughout the book explore the relationship betweenthe discussed material and related topics from mathematics,philosophy, 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 theory,pattern 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.

Autor*in

Sanjeev Kulkarni

Themen in »An Elementary Introduction to Statistical Learning Theory«

Computational & Graphical Statistics Data Mining Data Mining Statistics Electrical & Electronics Engineering Elektrotechnik u. Elektronik Mustererkennung Pattern Analysis Rechnergestützte u. graphische Statistik Statistics Statistik

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"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) "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)
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