Support Vector Machines

Name: Support Vector Machines
Brand: Springer-Verlag New York Inc.
SKU: 9780387772417
Price: 237.19 EUR
Availability: InStock

Автор: Ingo Steinwart, Andreas Christmann

Език:

Корици: С твърди корици

Издател: Springer-Verlag New York Inc.

Наличност: Външен склад

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237.19 € 463.91 лв

This book explains the principles that make support vector machines (SVMs) a successful modelling an...

Информация за книгата

Автор

Ingo Steinwart, Andreas Christmann

Език

Английски език

Корици

Книга - С твърди корици

Издадена

2008

страници

601

EAN

9780387772417

ISBN

0387772413

Enbook ID

02105825

Издател

Springer-Verlag New York Inc.

Теглоt

1054

Размери

163 x 241 x 38

Пълно описание

This book explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications. The authors present the basic ideas of SVMs together with the latest developments and current research questions in a unified style. They identify three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and their computational efficiency compared to several other methods.The book provides a unique in-depth treatment of both fundamental and recent material on SVMs that so far has been scattered in the literature. The book can thus serve as both a basis for graduate courses and an introduction for statisticians, mathematicians, and computer scientists. It further provides a valuable reference for researchers working in the field. The book covers all important topics concerning support vector machines such as: loss functions and their role in the learning process; reproducing kernel Hilbert spaces and their properties; a thorough statistical analysis that uses both traditional uniform bounds and more advanced localized techniques based on Rademacher averages and Talagrand's inequality; a detailed treatment of classification and regression; a detailed robustness analysis; and a description of some of the most recent implementation techniques. To make the book self-contained, an extensive appendix is added which provides the reader with the necessary background from statistics, probability theory, functional analysis, convex analysis, and topology.

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Support Vector Machines

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