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

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Cambridge University Press, Mar 23, 2000 - Computers - 189 pages
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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.
 

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Contents

The Learning Methodology
1
Linear Learning Machines
9
KernelInduced Feature Spaces
26
Generalisation Theory
52
Optimisation Theory
79
Support Vector Machines
93
Implementation Techniques
125
Applications of Support Vector Machines
149
A Pseudocode for the SMO Algorithm
162
References
173
Index
187
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About the author (2000)

Nello Cristianini is a Professor of Artificial Intelligence, University of Bristol.

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