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An elementary introduction to statistical learning theory [electronic resource] / Sanjeev Kulkarni, Gilbert Harman.

Kulkarni, Sanjeev.; Harman, Gilbert.
2011
Q325.5 .K85 2011eb
Available Online
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Title
An elementary introduction to statistical learning theory [electronic resource] / Sanjeev Kulkarni, Gilbert Harman.
Author
Kulkarni, Sanjeev.
ISBN
9781118023471 (electronic bk.)
1118023471 (electronic bk.)
9780470641835
9781118023433 (electronic bk.)
1118023439 (electronic bk.)
9781118023464 (ePub)
0470641835
1118023463
1283098687
9781283098687
Publication Details
Hoboken, N.J. : Wiley, c2011.
Language
English
Description
1 online resource (1 v.) : ill.
Call Number
Q325.5 .K85 2011eb
Dewey Decimal Classification
006.3/1
Summary
"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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Description based on print version record.
Added Author
Harman, Gilbert.
Series
Wiley series in probability and statistics.
Available in Other Form
Elementary introduction to statistical learning theory.
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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.

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