## Contents

# Support Vector Machines (SVM)

## Lectures on SVM

- ECE662, Spring 2010, Prof. Boutin: Lecture 22, 23
- ECE662, Spring 2008, Prof. Boutin: Lecture 11,12,13

## Lecture Notes on SVM

## Relevant Homework

## Useful Links

- LIBSVM - A library of SVM software, including both C and Matlab code. Various interfaces through several platforms available as well.

- A Practical Guide to Support Vector Classification: Mainly created for beginners, it quickly explains how to use the libsvm.

- svms.org:Here 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.