An elementary introduction to statistical learning theory

✍ Scribed by Sanjeev Kulkarni; Gilbert Harman; Wiley InterScience (Online service)

• Publisher: Wiley
• Year: 2011
• Tongue: English
• Leaves: 221
• Series: Wiley series in probability and statistics
• Category: Library

✦ Synopsis

"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. Read more... 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

✦ Table of Contents

ch00.pdf......Page 1
ch1.pdf......Page 14
ch2.pdf......Page 23
ch3.pdf......Page 36
ch4.pdf......Page 47
ch5.pdf......Page 56
ch6.pdf......Page 68
ch7.pdf......Page 78
ch8.pdf......Page 87
ch9.pdf......Page 99
ch10.pdf......Page 112
ch11.pdf......Page 129
ch12.pdf......Page 138
ch13.pdf......Page 150
ch14.pdf......Page 157
ch15.pdf......Page 163
ch16.pdf......Page 173
ch17.pdf......Page 185
ch18.pdf......Page 200
ch19.pdf......Page 210
ch20.pdf......Page 216
ch21.pdf......Page 219