Learning and soft computing : support vector machines, neural networks, and fuzzy logic models / Vojislav Kecman.
By: Kecman, V. (Vojislav)
.
Contributor(s): IEEE Xplore (Online Service) [distributor.] | MIT Press [publisher.].
Material type: 
BookSeries: Complex adaptive systems: Publisher: Cambridge, Massachusetts : MIT Press, c2001Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2001]Description: 1 PDF (xxxii, 541 pages) : illustrations.Content type: text Media type: electronic Carrier type: online resourceISBN: 9780262256513.Subject(s): Soft computing | Support vector machinesGenre/Form: Electronic books.Additional physical formats: Print version: No titleDDC classification: 006.3 Online resources: Abstract with links to resource Also available in print.Summary: 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.
"A Bradford book."
Includes bibliographical references (p. [531]-538) and index.
Restricted to subscribers or individual electronic text purchasers.
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.
Also available in print.
Mode of access: World Wide Web
Description based on PDF viewed 12/24/2015.
There are no comments for this item.