An Kulkarni, Sanjeev. creator Harman, Gilbert. text bibliography nju Hoboken, N.J Wiley c2011 2011 monographic eng

electronic

xi, 209 p.: ill. ; 24 cm. "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. 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. Sanjeev Kulkarni, Gilbert Harman. Includes bibliographical references and indexes. Machine learning Statistical methods Pattern recognition systems Q325.5 .K85 2011 006.31 KUE ST 300 9780470641835 (cloth) 0470641835 (cloth) 9781118023433 1118023439 9781118023464 1118023463 9781118023471 1118023471 DLC 101123 20150219124343.0 8834492