Difference between revisions of "Support Vector Machines Old Kiwi" - Rhea

Revision as of 10:19, 3 April 2008

Course Topics

This page and its subtopics discusses about Support Vector Machines

Lectures discussing Support Vector Machines: Lecture 11, Lecture 12 and Lecture 13.

Relevant Homework Homework 2_Old Kiwi

Useful Links

• Support Vector Machine on Wikipedia

• 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

Links

Links to many SWM softwares, tutorials, etc: Most of these sites are compilation of several links to codes on the web

1. SVM and Kernel Methods Matlab Toolbox http://asi.insa-rouen.fr/enseignants/~arakotom/toolbox/index.html

2. SVM - Support Vector Machines Software http://www.support-vector-machines.org/SVM_soft.html

3. Some SVM sample data http://www.cs.iastate.edu/~dcaragea/SVMVis/data_sets.htm

4. LIBSVM - A library of SVM software, including both C and Matlab code. Various interfaces through several platforms available as well. http://www.csie.ntu.edu.tw/~cjlin/libsvm/

Links to Matlab Toolbox tutorials

1. SVM Matlab Bioinformatics Toolbox http://www.mathworks.com/access/helpdesk/help/toolbox/bioinfo/index.html?/access/helpdesk/help/toolbox/bioinfo/ref/svmclassify.html&http://www.mathworks.com/cgi-bin/texis/webinator/search/

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.