Support Vector Machines

Name: Support Vector Machines
Brand: Springer-Verlag New York Inc.
SKU: 9780387772417
Price: 1239.79 RON
Availability: InStock

Autor: Ingo Steinwart, Andreas Christmann

Limbă:

Legare: Copertă tare

Editura: Springer-Verlag New York Inc.

Disponibilitate: În depozitul extern

Expediem în 10-13 zile

1 239.79 lei

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

Informații despre carte

Autor

Ingo Steinwart, Andreas Christmann

Limbă

engleză

Legare

Carte - Copertă tare

Publicat

2008

Pagini

601

EAN

9780387772417

ISBN

0387772413

Enbook ID

02105825

Editura

Springer-Verlag New York Inc.

Greutate

1054

Dimensiuni

163 x 241 x 38

Descriere completă

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.

S-ar putea să te intereseze

Clienții care au cumpărat această carte au mai cumpărat și

Cele mai căutate

Categories

Authors

Publishers

Produse

Produse

Cele mai căutate

Categories

Authors

Publishers

Produse

Produse

Support Vector Machines

Informații despre carte

Descriere completă

S-ar putea să te intereseze

Clienții care au cumpărat această carte au mai cumpărat și