Learning and Soft Computing

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.