Kulkarni / Harman An Elementary Introduction to Statistical Learning Theory

1. Auflage 2011
ISBN: 978-1-118-02343-3
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 232 Seiten, E-Book

Reihe: Wiley Series in Probability and Statistics

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.

Autoren/Hrsg.

Kulkarni, Sanjeev

Harman, Gilbert

Über Autor(innen)

SANJEEV KULKARNI, PhD, is Professor in the Department ofElectrical Engineering at Princeton University, where he is also anaffiliated faculty member in the Department of Operations Researchand Financial Engineering and the Department of Philosophy. Dr.Kulkarni has published widely on statistical pattern recognition,nonparametric estimation, machine learning, information theory, andother areas. A Fellow of the IEEE, he was awarded PrincetonUniversity's President's Award for Distinguished Teaching in2007.
GILBERT HARMAN, PhD, is James S. McDonnell DistinguishedUniversity Professor in the Department of Philosophy at PrincetonUniversity. A Fellow of the Cognitive Science Society, he is theauthor of more than fifty published articles in his areas ofresearch interest, which include ethics, statistical learningtheory, psychology of reasoning, and logic.