(Useful Links)
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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 ]
  
== Links ==
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* [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 ]
Links to many SWM softwares, tutorials, etc: Most of these sites are compilation of several links to codes on the web
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1. SVM and Kernel Methods Matlab Toolbox
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http://asi.insa-rouen.fr/enseignants/~arakotom/toolbox/index.html
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2. SVM - Support Vector Machines Software
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http://www.support-vector-machines.org/SVM_soft.html
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3. Some SVM sample data
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http://www.cs.iastate.edu/~dcaragea/SVMVis/data_sets.htm
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4. LIBSVM - A library of SVM software, including both C and Matlab code.  Various interfaces through several platforms available as well.
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http://www.csie.ntu.edu.tw/~cjlin/libsvm/
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Links to Matlab Toolbox tutorials
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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/
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== Journal References ==
 
== Journal References ==

Revision as of 11:30, 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

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

Alumni Liaison

Prof. Math. Ohio State and Associate Dean
Outstanding Alumnus Purdue Math 2008

Jeff McNeal