The MIT Press, Cambridge, MA, 2001
608 pp., 268 illus.,
This is the first textbook that provides
a thorough, comprehensive and unified introduction to the field
of learning from experimental data and soft computing.
Support vector machines (SVMs)
and neural networks (NNs) 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 SVMs, NNs, and FLS as parts
of a connected whole. The theory and algorithms are illustrated
by 47 practical examples, as well as by 155 problem sets and
simulated experiments. This approach enables the reader to develop
SVMs, NNs, and FLS in addition to understanding them. The book
also presents three case studies on: NNs 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.
Learning and Soft Computing provides a clearly organised book focusing
on a broad range of algorithms and it is aimed at senior undergraduate students,
graduate students and practising researchers and scientists who want to use and
develop SVMs, NNs and/or FL models rather than simply study them.
The book is rich in graphical presentations (268 illustrations).
The insight obtained through the simulation experimenting and graphical presentation
of the results enables faster and better understanding of initially 'difficult'
matrix-vectorial notations present in the field. This, we hope, will enable self-study