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

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 organized book focusing on a broad range of algorithms and it is aimed at senior undergraduate students, graduate students and practicing researchers and scientists who want to use and develop SVMs, NNs and/or FL models rather than simply study them. The book is reach 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 too.