Classification and Regularization in Learning Theory : Concepts, Algorithms, and Analysis

Qiang Wu

ISBN 10: 3639198352 / ISBN 13: 9783639198355

Published by VDM Verlag Dr. Müller, 2009

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9783639198355

Bibliographic Details

• Title: Classification and Regularization in Learning Theory : Concepts, Algorithms, and Analysis
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2009
• Language: English
• ISBN 10: 3639198352
• ISBN 13: 9783639198355
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 22W x 15H x 0.9D centimeters 22 centimeters width by 15 centimeters height by 0.9 centimeters depth
• Weight: 222 grams

About this title

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.

About the Author

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?