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Kulkarni, Sanjeev.

An elementary introduction to statistical learning theory / Sanjeev Kulkarni, Gilbert Harman. - Hoboken, N.J. : Wiley, c2011. - xi, 209 p.: ill. ; 24 cm. - Wiley series in probability and statistics . - Wiley series in probability and statistics. .

Includes bibliographical references and indexes.

Introduction: Classification, Learning, Features, and Applications -- Probability -- Probability Densities -- The Pattern Recognition Problem -- The Optimal Bayes Decision Rule -- Learning from Examples -- The Nearest Neighbor Rule -- Kernel Rules -- Neural Networks: Perceptrons -- Multilayer Networks -- PAC Learning -- VC Dimension -- Infinite VC Dimension -- The Function Estimation Problem -- Learning Function Estimation -- Simplicity -- Support Vector Machines -- Boosting -- Bibliography.

"A joint endeavor from leading researchers in the fields of philosophy and electrical engineering An Introduction to Statistical Learning Theory provides a broad and accessible introduction to rapidly evolving field of statistical pattern recognition and statistical learning theory. Exploring topics that are not often covered in introductory level books on statistical learning theory, including PAC learning, VC dimension, and simplicity, the authors present upper-undergraduate and graduate levels with the basic theory behind contemporary machine learning and uniquely suggest it serves as an excellent framework for philosophical thinking about inductive inference"--Back cover.

9780470641835 (cloth) 0470641835 (cloth) 9781118023433 1118023439 9781118023464 1118023463 9781118023471 1118023471 = An elementary introduction to statistical learning theory


Machine learning--Statistical methods.
Pattern recognition systems.

Q325.5 / .K85 2011

006.31 / KUE
Last Updated on September 15, 2019
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