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Learning With Kernels

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Learning with Kernels

Learning with Kernels
Author :
Publisher : MIT Press
Total Pages : 658
Release :
ISBN-10 : 0262194759
ISBN-13 : 9780262194754
Rating : 4/5 (59 Downloads)
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Book Synopsis Learning with Kernels by : Bernhard Schölkopf

Download or read book Learning with Kernels written by Bernhard Schölkopf and published by MIT Press. This book was released on 2002 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to Support Vector Machines and related kernel methods.


Learning with Kernels Related Books

Learning with Kernels
Language: en
Pages: 658

Learning with Kernels

Authors: Bernhard Schölkopf
Categories: Computers
Type: BOOK - Published: 2002 - Publisher: MIT Press

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A comprehensive introduction to Support Vector Machines and related kernel methods.
Regularization, Optimization, Kernels, and Support Vector Machines
Language: en
Pages: 528

Regularization, Optimization, Kernels, and Support Vector Machines

Authors: Johan A.K. Suykens
Categories: Computers
Type: BOOK - Published: 2014-10-23 - Publisher: CRC Press

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Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing
Kernel Methods for Pattern Analysis
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
Kernel Methods in Computational Biology
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.
Learning with Support Vector Machines
Language: en
Pages: 97

Learning with Support Vector Machines

Authors: Colin Campbell
Categories: Computers
Type: BOOK - Published: 2011 - Publisher: Morgan & Claypool Publishers

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Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of a