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An introduction to Support Vector Machines

An introduction to Support Vector Machines
An introduction to Support Vector Machines
This book is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. The book also introduces Bayesian analysis of learning and relates SVMs to Gaussian Processes and other kernel based learning methods. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc. Their first introduction in the early 1990s lead to a recent explosion of applications and deepening theoretical analysis, that has now established Support Vector Machines along with neural networks 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 application of these techniques. The concepts are introduced gradually in accessible and self-contained stages, though in each stage the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally the book will equip the practitioner to apply the techniques and an associated web site will provide pointers to updated literature, new applications, and on-line software.
0521780195
Cambridge University Press
Cristianini, N.
00885da7-7833-4f0c-b8a0-3f385d89f642
Shawe-Taylor, J.
c32d0ee4-b422-491f-8c28-78663851d6db
Cristianini, N.
00885da7-7833-4f0c-b8a0-3f385d89f642
Shawe-Taylor, J.
c32d0ee4-b422-491f-8c28-78663851d6db

Cristianini, N. and Shawe-Taylor, J. (2000) An introduction to Support Vector Machines , Cambridge University Press

Record type: Book

Abstract

This book is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. The book also introduces Bayesian analysis of learning and relates SVMs to Gaussian Processes and other kernel based learning methods. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc. Their first introduction in the early 1990s lead to a recent explosion of applications and deepening theoretical analysis, that has now established Support Vector Machines along with neural networks 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 application of these techniques. The concepts are introduced gradually in accessible and self-contained stages, though in each stage the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally the book will equip the practitioner to apply the techniques and an associated web site will provide pointers to updated literature, new applications, and on-line software.

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More information

Published date: March 2000
Additional Information: Address: Cambridge, UK
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 259578
URI: https://eprints.soton.ac.uk/id/eprint/259578
ISBN: 0521780195
PURE UUID: 72e38545-8ba6-4642-ae6b-042acda985c2

Catalogue record

Date deposited: 02 Mar 2005
Last modified: 18 Jul 2017 09:20

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Contributors

Author: N. Cristianini
Author: J. Shawe-Taylor

University divisions

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