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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)

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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) Cover

 

Synopses & Reviews

Publisher Comments:

andlt;Pandgt;In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs — -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.andlt;/Pandgt;

Synopsis:

A comprehensive introduction to Support Vector Machines and related kernel methods.

Synopsis:

andlt;Pandgt;A comprehensive introduction to Support Vector Machines and related kernel methods.andlt;/Pandgt;

Synopsis:

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs — -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

About the Author

Bernhard Schlkopf is Managing Director of the Max Planck Institute for Biological Cybernetics in Tübingen, Germany.Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.

Product Details

ISBN:
9780262194754
Subtitle:
Support Vector Machines, Regularization, Optimization, and Beyond
Author:
Scholkopf, Bernhard
Author:
Sch�lkopf, Bernhard
Author:
Schalkopf, Bernhard
Author:
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Author:
sch
Author:
ouml
Author:
lkopf, Bernhard
Author:
Smola, Alexander J.
Publisher:
The MIT Press
Location:
Cambridge, Mass.
Subject:
General
Subject:
Computer Science
Subject:
Artificial Intelligence
Subject:
Algorithms
Subject:
Machine learning
Subject:
Kernel functions
Subject:
Artificial Intelligence - General
Subject:
Intelligence (AI) & Semantics
Subject:
Personal Computers-General
Copyright:
Series:
Adaptive Computation and Machine Learning series Learning with Kernels
Series Volume:
9455
Publication Date:
20011207
Binding:
Hardback
Grade Level:
from 17
Language:
English
Illustrations:
138 illus.
Pages:
648
Dimensions:
10 x 8 in

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Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Computers and Internet » Personal Computers » General
Computers and Internet » Software Engineering » Programming and Languages
Humanities » Philosophy » General
Science and Mathematics » Environmental Studies » Environment
Science and Mathematics » History of Science » General
Science and Mathematics » Mathematics » General

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) New Hardcover
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Product details 648 pages MIT Press - English 9780262194754 Reviews:
"Synopsis" by , A comprehensive introduction to Support Vector Machines and related kernel methods.
"Synopsis" by , andlt;Pandgt;A comprehensive introduction to Support Vector Machines and related kernel methods.andlt;/Pandgt;
"Synopsis" by , In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs — -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.
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