(Useful Links)
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* [http://www.svms.org/software.html 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.
 
* [http://www.svms.org/software.html 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.
  
* [http://www2.lib.purdue.edu:2483/10.1145/130385.130401 Purdue link to SVM]  
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* [http://www2.lib.purdue.edu:2483/10.1145/130385.130401 Purdue link to SVM]
  
* [http://doi.acm.org/10.1145/130385.130401
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* [http://doi.acm.org/10.1145/130385.130401 ACM link to SVM]
ACM link to SVM]
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* Journal References
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== 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.
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* 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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* 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.

Revision as of 08:34, 24 March 2008

This page and its subtopics discusses about Support Vector Machines

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


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.

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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