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An elementary introduction to statistical learning theory / [electronic resource]

by Kulkarni, Sanjeev; Harman, Gilbert; Wiley InterScience (Online service).
Material type: materialTypeLabelBookSeries: Wiley series in probability and statistics: Publisher: Hoboken, N.J. : Wiley, ©2011Description: 1 online resource (1 volume) : illustrations.ISBN: 9781118023471; 1118023471; 9781118023433; 1118023439; 1283098687; 9781283098687.Subject(s): Machine learning -- Statistical methods | Pattern recognition systems | Artificial Intelligence | Pattern Recognition, Automated | Statistics as Topic | Aprenentatge automàtic -- Mètodes estadístics | Reconeixement de formes (Informàtica) | COMPUTERS -- Enterprise Applications -- Business Intelligence Tools | COMPUTERS -- Intelligence (AI) & Semantics | Machine learning -- Statistical methods | Pattern recognition systems | Maschinelles Lernen | Statistik | Llibres electrònics | Electronic booksOnline resources: Wiley Online Library
Contents:
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.
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.
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Includes bibliographical references and index.

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.

"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.

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Last Updated on September 15, 2019
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