Support Vector Machines

Name: Support Vector Machines
Brand: Springer-Verlag New York Inc.
SKU: 9780387772417
Price: 86651 HUF
Availability: InStock

Szerző: Ingo Steinwart, Andreas Christmann

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer-Verlag New York Inc.

Elérhetőség: Beszállítói készleten

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

Információk a könyvről

Szerző

Ingo Steinwart, Andreas Christmann

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2008

oldal

601

EAN

9780387772417

ISBN

0387772413

Enbook ID

02105825

Kiadó

Springer-Verlag New York Inc.

Súly

1054

Méretek

163 x 241 x 38

Teljes leírás

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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