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

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MIT Press, 2001 - Computers - 541 pages
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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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soft computing

Contents

Rationale Motivations Needs Basics
1
Problems
103
Simulation Experiments
117
Problems
184
Problems
244
Simulation Experiments
253
Algorithm
266
Problems
303
Simulation Experiments
309
Problems
358
Problems
410
Simulation Experiments
419
Basic Nonlinear Optimization Methods
481
Mathematical Tools of Soft Computing
505
Selected Abbreviations
525
Copyright

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About the author (2001)

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

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