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An elementary introduction to statis...

Harman, Gilbert.

An elementary introduction to statistical learning theory /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	An elementary introduction to statistical learning theory /Sanjeev Kulkarni, Gilbert Harman.
Author:	Kulkarni, Sanjeev.
other author:	Harman, Gilbert.
Published:	Hoboken, N.J. :Wiley,c2011.
Description:	xiv, 209 p. :ill. ;24 cm.
Subject:	Machine learningStatistical methods.
Online resource:	Inhaltsverzeichnis
Online resource:	http://www.loc.gov/catdir/enhancements/fy1210/2010045223-t.html
Online resource:	http://www.loc.gov/catdir/enhancements/fy1210/2010045223-b.html
Online resource:	http://www.loc.gov/catdir/enhancements/fy1210/2010045223-d.html
ISBN:	9780470641835 :

An elementary introduction to statistical learning theory /
Kulkarni, Sanjeev.

An elementary introduction to statistical learning theory /Sanjeev Kulkarni, Gilbert Harman. - Hoboken, N.J. :Wiley,c2011. - xiv, 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.

ISBN: 9780470641835 :$126.95

LCCN: 2010045223Subjects--Topical Terms:

305185
Machine learning
--Statistical methods.

LC Class. No.: Q325.5 / .K85 2011

Dewey Class. No.: 006.3/1

An elementary introduction to statistical learning theory /
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020 $z 9781118023433 (ePDF)
020 $z 9781118023464 (ePub)
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050 0 0 $a Q325.5 $b .K85 2011
082 0 0 $a 006.3/1 $2 22
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100 1 $a Kulkarni, Sanjeev. $3 304941
245 1 3 $a An elementary introduction to statistical learning theory / $c Sanjeev Kulkarni, Gilbert Harman.
260 $a Hoboken, N.J. : $b Wiley, $c c2011.
300 $a xiv, 209 p. : $b ill. ; $c 24 cm.
490 1 $a Wiley series in probability and statistics
504 $a Includes bibliographical references and indexes.
505 0 $a 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.
520 $a "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.
650 0 $a Machine learning $x Statistical methods. $3 305185
650 0 $a Pattern recognition systems. $3 183725
700 1 $a Harman, Gilbert. $3 304942
830 0 $a Wiley series in probability and statistics. $3 324851
856 $u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=024567239&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA $z Inhaltsverzeichnis
856 4 1 $3 Table of contents only $u http://www.loc.gov/catdir/enhancements/fy1210/2010045223-t.html
856 4 2 $3 Contributor biographical information $u http://www.loc.gov/catdir/enhancements/fy1210/2010045223-b.html
856 4 2 $3 Publisher description $u http://www.loc.gov/catdir/enhancements/fy1210/2010045223-d.html

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http://www.loc.gov/catdir/enhancements/fy1210/2010045223-t.html
http://www.loc.gov/catdir/enhancements/fy1210/2010045223-b.html
http://www.loc.gov/catdir/enhancements/fy1210/2010045223-d.html

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