Support vector machines (SVMs), were originally formulated for two-class classification problems, and have been accepted as a powerful tool for developing pattern classification and function approximations systems. This book provides a unique perspective of the state of the art in SVMs by taking the only approach that focuses on classification rather than covering the theoretical aspects.

The book clarifies the characteristics of two-class SVMs through their extensive analysis, presents various useful architectures for multiclass classification and function approximation problems, and discusses kernel methods for improving generalization ability of conventional neural networks and fuzzy systems. Ample illustrations, examples and computer experiments are included to help readers understand the new ideas and their usefulness.

This book supplies a comprehensive resource for the use of SVMs in pattern classification and will be invaluable reading for researchers, developers & students in academia and industry.

I was shocked to see a student’s report on performance comparisons between support vector machines (SVMs) and fuzzy classifiers that we had developed with our best endeavors. Classification performance of our fuzzy classifiers was comparable, but in most cases inferior, to that of support vector machines. This tendency was especially evident when the numbers of class data were small. I shifted my research efforts from developing fuzzy classifiers with high generalization ability to developing support vector machine–based classifiers.

This book focuses on the application of support vector machines to pattern classification. Specifically, we discuss the properties of support vector machines that are useful for pattern classification applications, several multiclass models, and variants of support vector machines. To clarify their applicability to real-world problems, we compare performance of most models discussed in the book using real-world benchmark data. Readers interested in the theoretical aspect of support vector machines should refer to books such as [109, 215, 256, 257].