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Classification and Regularization in Learning Theory: Concepts, Algorithms, and Analysis - Couverture souple

Wu, Qiang

 
9783639198355: Classification and Regularization in Learning Theory: Concepts, Algorithms, and Analysis
Couverture souple
ISBN 10 : 3639198352 ISBN 13 : 9783639198355
Editeur : VDM Verlag, 2009

Synopsis

Over the last decade, statistical learning theory has achieved rapid progress due to the introduction and research of classification algorithms including support vector machines and boosting. Along with their successful applications in practice, theoretical performance of these algorithms becomes well understood in terms of margin bounds, Bayes risk consistency, and asymptotic rate analysis. This monograph provides further investigation of these algorithms within regularization frameworks and from an approximation theory point of view. Error analysis frameworks by error decomposition techniques are fully developed for two classes of regularization schemes which cover the support vector machines, regularized boosting, and support vector kernel networks by linear programming and indefinite kernels. The results presented in this monograph are by far the best. The error analysis frameworks have been shown to be wide applicable in most recent research works and should be able to shed light on future researches on related topics in machine learning and artificial intelligence.

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Présentation de l'éditeur

Over the last decade, statistical learning theory has achieved rapid progress due to the introduction and research of classification algorithms including support vector machines and boosting. Along with their successful applications in practice, theoretical performance of these algorithms becomes well understood in terms of margin bounds, Bayes risk consistency, and asymptotic rate analysis. This monograph provides further investigation of these algorithms within regularization frameworks and from an approximation theory point of view. Error analysis frameworks by error decomposition techniques are fully developed for two classes of regularization schemes which cover the support vector machines, regularized boosting, and support vector kernel networks by linear programming and indefinite kernels. The results presented in this monograph are by far the best. The error analysis frameworks have been shown to be wide applicable in most recent research works and should be able to shed light on future researches on related topics in machine learning and artificial intelligence.

Biographie de l'auteur

Qiang Wu, Ph.D: Studied applied mathematics at City University of Hong Kong. Research fields include statistical learning theory, high dimensional data analysis, and related interdisciplinary areas. Currently a visiting assistant professor in the Department of Mathematics, Michigan State University?

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
VDM Verlag
Date d'édition
2009
Langue
anglais
ISBN 10 
3639198352
ISBN 13 
9783639198355
Reliure
Broché
Nombre de pages
160
Coordonnées du fabricant
non disponible
Personne responsable
non disponible