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
3 Reviews
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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Review: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods

User Review  - Joecolelife - Goodreads

I just happened to read the reviews on the book on Support vector machines by Nello Cristianini and John Shawe-Taylor. Could not resist adding my own comments about the book. Excellent book. I plan to ... Read full review

Review: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods

User Review  - Hungttbk - Goodreads

ok Read full review

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
Copyright

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About the author (2000)

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

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