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Könyv Classification and Regularization in Learning Theory Qiang Wu

Classification and Regularization in Learning Theory

Concepts, Algorithms, and Analysis

Szerző: Qiang Wu
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: VDM Verlag Dr. Müller
Elérhetőség: Kiadói készleten rendelésre
Küldés 17-27 napon belül
20 516 Ft
Over the last decade, statistical learning theory has achieved rapid progress due to the introductio...

Információk a könyvről

Szerző
Qiang Wu
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2009
oldal
160
EAN
9783639198355
Enbook ID
06827882
Kiadó
VDM Verlag Dr. Müller
Súly
222
Méretek
150 x 220 x 10

Teljes leírás

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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