Kernel Methods

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Kernel Methods for Pattern Analysis

Author	: John Shawe-Taylor
Publisher	: Cambridge University Press
Total Pages	: 520
Release	: 2004-06-28
ISBN-10	: 0521813972
ISBN-13	: 9780521813976
Rating	: 4/5 (976 Downloads)

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Book Synopsis Kernel Methods for Pattern Analysis by : John Shawe-Taylor

Download or read book Kernel Methods for Pattern Analysis written by John Shawe-Taylor and published by Cambridge University Press. This book was released on 2004-06-28 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description

Kernel Methods for Pattern Analysis Related Books

Language: en
Pages: 520

Kernel Methods for Pattern Analysis

Authors: John Shawe-Taylor

Categories: Computers

Type: BOOK - Published: 2004-06-28 - Publisher: Cambridge University Press

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Publisher Description

Language: en
Pages: 428

Kernel Methods in Computational Biology

Authors: Bernhard Schölkopf

Categories: Computers

Type: BOOK - Published: 2004 - Publisher: MIT Press

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A detailed overview of current research in kernel methods and their application to computational biology.

Language: en
Pages: 617

Kernel Methods and Machine Learning

Authors: S. Y. Kung

Categories: Computers

Type: BOOK - Published: 2014-04-17 - Publisher: Cambridge University Press

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Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for

Language: en
Pages: 645

Learning with Kernels

Authors: Bernhard Scholkopf

Categories: Computers

Type: BOOK - Published: 2018-06-05 - Publisher: MIT Press

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A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on resul

Language: en
Pages: 434

Kernel Methods for Remote Sensing Data Analysis

Authors: Gustau Camps-Valls

Categories: Technology & Engineering

Type: BOOK - Published: 2009-09-03 - Publisher: John Wiley & Sons

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Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard