Kernel Methods for Pattern Analysis

Nonfiction, Computers, Advanced Computing, Engineering, Optical Data Processing, Computer Vision, General Computing
Cover of the book Kernel Methods for Pattern Analysis by John Shawe-Taylor, Nello Cristianini, Cambridge University Press
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Author: John Shawe-Taylor, Nello Cristianini ISBN: 9781139636940
Publisher: Cambridge University Press Publication: June 28, 2004
Imprint: Cambridge University Press Language: English
Author: John Shawe-Taylor, Nello Cristianini
ISBN: 9781139636940
Publisher: Cambridge University Press
Publication: June 28, 2004
Imprint: Cambridge University Press
Language: English

Kernel 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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Kernel 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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