April 1st, 2010

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• Maximizing margins.
• Soft margins.
• Kernels.
• Kernels everywhere.

Many researchers have first approached SVMs with Chris Burges' tutorial. Section 1 to 4 of the Bottou and Lin chapter propose a self-contained mathematical treatment of duality in SVMs.

• (optional) C. J. C. Burges: A Tutorial on Support Vector Machines for Pattern Recognition. 1998.

• (optional) L. Bottou and C.-J. Lin: Support Vector Machine Solvers. In Large Scale Kernel Machines, MIT Press, 2007. (section 1-4)

The most used softwares for SVMs are LibSVM and SVMLight. Also the SVQP2 solver is a small and efficient C++ library for solving the SVM dual optimization problem. Note that one should use very different algorithms for training SVMs with kernels and SVMs with linear parametrization.