Learning and Soft Computing
Support Vector Machines, Neural Networks, and Fuzzy Logic Models
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.
Downloadable instructor resources available for this title: solution manual
HardcoverOut of Print ISBN: 9780262112550 576 pp. | 7 in x 9 in 268 illus.
Paperback$50.00 X | £40.00 ISBN: 9780262527903 576 pp. | 7 in x 9 in 268 illus.
Kecman has many years of teaching and research experience, so naturally he does an excellent job of presenting the essence of learning and soft computing using neural networks, fuzzy logic, and statistics.
Department of Electrical and Computer Engineering, Rutgers University
This book provides an excellent in-depth description of modern learning and soft computing methodologies. Accompanying software implementation of learning algorithms makes this text especially valuable for practitioners and graduate students interested in applications of predictive learning.
Department of Electrical and Computer Engineering, University of Minnesota, Twin Cities
This outstanding volume unifies the concepts of learning, neural networks, support vector machines, and fuzzy logic! It offers a clear presentation and numerous examples followed by end-of-chapter problems. These things along with the accompanying software make the book a favorite candidate for the leading academic text and an indispensable reference for soft computing professionals.
Jacek M. Zurada
S.T. Fife Professor of Electrical and Computer Engineering, University of Louisville, and Editor-in-Chief, IEEE Transactions on Neural Networks