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Other titles in the Complex Adaptive Systems series:

Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems)

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Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems) Cover

 

Synopses & Reviews

Publisher Comments:

andlt;Pandgt;This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.andlt;/Pandgt;

Synopsis:

This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.

Description:

Includes bibliographical references (p. [531]-538) and index.

About the Author

Vojislav Kecman is Associate Professor in the School of Engineering at Virginia Commonwealth University.

Product Details

ISBN:
9780262112550
Author:
Kecman, V.
Author:
Kecman, Vojislav
Publisher:
Bradford Book
Location:
Cambridge, Mass.
Subject:
Artificial Intelligence
Subject:
Neural Networks
Subject:
Soft computing
Subject:
Artificial Intelligence - General
Subject:
Intelligence (AI) & Semantics
Subject:
support vector machines
Subject:
Computers-Reference - General
Edition Description:
Includes bibliographical references and index.
Series:
Complex Adaptive Systems Learning and Soft Computing
Series Volume:
B-88-4
Publication Date:
20010631
Binding:
HARDCOVER
Grade Level:
Professional and scholarly
Language:
English
Illustrations:
268 illus.
Pages:
576
Dimensions:
9 x 7 in
Age Level:
from 18

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Related Subjects

Computers and Internet » Artificial Intelligence » General
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Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems) New Hardcover
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Product details 576 pages Bradford Book - English 9780262112550 Reviews:
"Synopsis" by , This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.
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