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* [http://www.cs.iastate.edu/~dcaragea/SVMVis/data_sets.htm Some SVM sample data ] | * [http://www.cs.iastate.edu/~dcaragea/SVMVis/data_sets.htm Some SVM sample data ] | ||
| − | + | * [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/ SVM Matlab Bioinformatics Toolbox ] | |
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== Journal References == | == Journal References == | ||
Revision as of 10:30, 3 April 2008
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_OldKiwi
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.
