An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Nello Cristianini et John Shawe-Taylor

ISBN 10: 0521780195 / ISBN 13: 9780521780193

Published by Cambridge University Press, 2000

About this Item

Ancien livre de bibliothèque avec équipements. Edition 2000. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 2000. Ammareal gives back up to 15% of this item's net price to charity organizations.

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Bibliographic Details

• Title: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
• Publisher: Cambridge University Press
• Publication Date: 2000
• Language: English
• ISBN 10: 0521780195
• ISBN 13: 9780521780193
• Binding: Hardcover
• Condition: Très bon

About this title

Synopsis

This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software make it an ideal starting point for further study.

Book Description

This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications.