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

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



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




Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models Vojislav Kecman
Language: English
Page: 576
Format: pdf
ISBN: 0262112558, 9780262112550
Publisher: The MIT Press

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 in the Department of Mechanical Engineering, University of Auckland.

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