Kernel Methods for Pattern Analysis
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Kernel Methods for Pattern Analysis by John Shawe-Taylor
Book DescriptionKernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.
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Book DetailsISBN: 9780521813976
(247mm x 174mm x 27mm)
Imprint: Cambridge University Press
Publisher: Cambridge University Press
Publish Date: 28-Jun-2004
Country of Publication: United Kingdom
Books By Author John Shawe-Taylor
Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Hardback (March 2000)» View all books by John Shawe-Taylor
This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.
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Author Biography - John Shawe-Taylor
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