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010 a| 2010045223;

020 a| 9780470641835 (cloth);

020 a| 0470641835 (cloth);

020 a| 9781118023433;

020 a| 1118023439;

020 a| 9781118023464;

020 a| 1118023463;

020 a| 9781118023471;

020 a| 1118023471;

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100 1 a| Kulkarni, Sanjeev.=| ^A714615;

245 13 a| An elementary introduction to statistical learning theoryh| [electronic resource] /c| Sanjeev Kulkarni, Gilbert Harman.;

260 a| Hoboken, N.J. :b| Wiley,;

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.;

506 a| Available only to authorized users.;

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.;

538 a| Mode of access: World Wide Web;

650 0 a| Machine learningx| Statistical methods.=| ^A145211;

650 0 a| Pattern recognition systems.=| ^A37220;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Harman, Gilbert.=| ^A49526;

830 0 a| Wiley series in probability and statistics.=| ^A373775;

856 40 z| Full text available from Ebook Central - Academic Completeu| https://ebookcentral.proquest.com/lib/eastcarolina/detail.action?docID=697570;

947 a| (OCoLC)ocn685239939;

949 a| CLICK ON WEB ADDRESSw| ASISh| JOYNER188;

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999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4441208-2001l| HSLELECm| HSLr| Ys| Yt| HEBKu| 6/29/2016x| EBOOKz| HERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4441208-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 6/29/2016x| EBOOKz| JERESOURCE;

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

Author/creator	Kulkarni, Sanjeev
Other author	Harman, Gilbert.
Format	Electronic
Publication Info	Hoboken, N.J. : Wiley,
Description	xiv, 209 p. : ill. ; 24 cm.
Supplemental Content	Full text available from Ebook Central - Academic Complete
Subjects	Machine learning--Statistical methods. Pattern recognition systems.

Series	Wiley series in probability and statistics Wiley series in probability and statistics. ^A373775
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 -- Bibliography.
Abstract	"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 note	Includes bibliographical references and indexes.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2010045223
ISBN	9780470641835 (cloth)
ISBN	0470641835 (cloth)
ISBN	9781118023433
ISBN	1118023439
ISBN	9781118023464
ISBN	1118023463
ISBN	9781118023471
ISBN	1118023471

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

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