(Useful Links)
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* [http://doi.acm.org/10.1145/130385.130401 ACM link to SVM]
 
* [http://doi.acm.org/10.1145/130385.130401 ACM link to SVM]
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* [http://asi.insa-rouen.fr/enseignants/~arakotom/toolbox/index.html SVM and Kernel Methods Matlab Toolbox]
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* [http://www.support-vector-machines.org/SVM_soft.html SVM - Support Vector Machines Software]
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* [http://www.cs.iastate.edu/~dcaragea/SVMVis/data_sets.htm Some SVM sample data ]
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* [http://www.csie.ntu.edu.tw/~cjlin/libsvm/ LIBSVM ] - A library of SVM software, including both C and Matlab code.  Various interfaces through several platforms available as well.
  
 
== Links ==
 
== Links ==

Revision as of 11:23, 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

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

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.

Alumni Liaison

Abstract algebra continues the conceptual developments of linear algebra, on an even grander scale.

Dr. Paul Garrett