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

Nonfiction, Computers, Advanced Computing, Computer Science, General Computing
Cover of the book An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by Nello Cristianini, John Shawe-Taylor, Cambridge University Press
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Author: Nello Cristianini, John Shawe-Taylor ISBN: 9781139636773
Publisher: Cambridge University Press Publication: March 23, 2000
Imprint: Cambridge University Press Language: English
Author: Nello Cristianini, John Shawe-Taylor
ISBN: 9781139636773
Publisher: Cambridge University Press
Publication: March 23, 2000
Imprint: Cambridge University Press
Language: English

This is the first comprehensive introduction to Support Vector Machines (SVMs), a 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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This is the first comprehensive introduction to Support Vector Machines (SVMs), a 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.

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