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Classification and Regularization in Learning Theory

Name: Classification and Regularization in Learning Theory
Brand: VDM Verlag Dr. Müller
SKU: 9783639198355
Price: 305.27 RON
Availability: InStock

Concepts, Algorithms, and Analysis

Autor: Qiang Wu

Limbă:

Legare: Carte broșată

Editura: VDM Verlag Dr. Müller

Disponibilitate: La editor doar la comandă

Expediem în 17-27 zile

305.27 lei

Over the last decade, statistical learning theory has achieved rapid progress due to the introductio...

Informații despre carte

Autor

Qiang Wu

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2009

Pagini

160

EAN

9783639198355

Enbook ID

06827882

Editura

VDM Verlag Dr. Müller

Greutate

222

Dimensiuni

150 x 220 x 10

Descriere completă

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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Classification and Regularization in Learning Theory

Concepts, Algorithms, and Analysis

Informații despre carte

Descriere completă

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