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.