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

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Hardcover | 15 April 2000

At a Glance

Hardcover
204 Pages

Dimensions(cm)
24.41 x 16.99 x 1.27

Hardcover

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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. 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 ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.

Industry Reviews

'... the most accessible introduction to the area I have yet seen'. D. J. Hand, Publication of the International Statistical Institute 'The book is an admirable presentation of this powerful new approach to pattern classification.' Alex M. Andrew, Robotica ' ... an excellent book, complete and readable without big requirements in mathematical functional analysis.' Zentralblatt fur Mathematik und ihre Grenzgebiete Mathematics Abstracts

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You Can Find This Book In

Non-Fiction Mathematics Computing & I.T.Computer Science Artificial Intelligence Machine Learning Pattern Recognition Databases Data Capture & Analysis Computer Programming & Software Development

Preface
Notation
The Learning Methodology	p. 1
Linear Learning Machines	p. 9
Kernel-Induced Feature Spaces	p. 26
Generalisation Theory	p. 52
Optimisation Theory	p. 79
Support Vector Machines	p. 93
Implementation Techniques	p. 125
Applications of Support Vector Machines	p. 149
Pseudocode for the SMO Algorithm	p. 162
Background Mathematics	p. 165
References	p. 173
Index	p. 187
Table of Contents provided by Blackwell. All Rights Reserved.

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

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

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