Support Vector Machines: Optimization Based Theory, Algorithms,
and Extensions presents an accessible treatment of the two main
components of support vector machines (SVMs) classification
problems and regression problems. The book emphasizes the close
connection between optimization theory and SVMs since optimization
is one of the pillars on which SVMs are built.
The authors share insight on many of their research
achievements. They give a precise interpretation of statistical
leaning theory for C-support vector classification. They also
discuss regularized twin SVMs for binary classification problems,
SVMs for solving multi-classification problems based on ordinal
regression, SVMs for semi-supervised problems, and SVMs for
problems with perturbations.
To improve readability, concepts, methods, and results are
introduced graphically and with clear explanations. For important
concepts and algorithms, such as the Crammer-Singer SVM for
multi-class classification problems, the text provides geometric
interpretations that are not depicted in current literature.
Enabling a sound understanding of SVMs, this book gives
beginners as well as more experienced researchers and engineers the
tools to solve real-world problems using SVMs.
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