Learning and Soft Computing : Support Vector Machines, Neural Networks, and Fuzzy Logic Models

Vojislav Kecman

ISBN 10: 0262527901 / ISBN 13: 9780262527903

Published by MIT Press, 2001

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9780262527903

Bibliographic Details

• Title: Learning and Soft Computing : Support Vector Machines, Neural Networks, and Fuzzy Logic Models
• Publisher: MIT Press
• Publication Date: 2001
• Language: English
• ISBN 10: 0262527901
• ISBN 13: 9780262527903
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 178H x 31D millimeters 229 millimeters width by 178 millimeters height by 31 millimeters depth
• Weight: 963 grams

About this title

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.

About the Author

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