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

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

Relevant Homework

• HW2, ECE662, Spring 2008, Prof. Boutin

Useful Links

• Support Vector Machine on Wikipedia

• 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.

• A Tutorial on Support Vector Machines for Pattern Recognition

• Support Vector Machines for 3D Object Recognition

• 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.

• Purdue link to SVM

• ACM link to SVM

• SVM and Kernel Methods Matlab Toolbox

• SVM - Support Vector Machines Software

• Some SVM sample data

• SVM Matlab Bioinformatics Toolbox

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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