Support Vector Machines (SVM)

Lectures on SVM

Lecture Notes on SVM

  • ECE662, Spring 2008, Prof. Boutin: Lecture 11,12,13

Relevant Homework

Useful Links

  • LIBSVM - A library of SVM software, including both C and Matlab code. Various interfaces through several platforms available as well.
  • is a good webpage containing links to effective Support Vector Machines packages, written in C/C++. Matlab, applicable for binary/multi- calss classifications.

Journal References

  • M.A. Aizerman, E.M. Braverman, L.I. Rozoner. Theoretical foundations of the potential function method in pattern recognition learning. Automation and Control, 1964, Vol. 25, pp. 821-837.
  • Bernhard E. Boser and Isabelle M. Guyon and Vladimir N. Vapnik. A training algorithm for optimal margin classifiers. COLT '92: Proceedings of the fifth annual workshop on Computational learning theory. 1992. Pittsburgh, PA.

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