Revision as of 08:27, 24 March 2008 by Lbachega (Talk)

This page and its subtopics discusses about Support Vector Machines

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

  • Other related sites:

`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 <http://citeseer.ist.psu.edu/cache/papers/cs/26235/http:zSzzSzwww.isi.uu.nlzSzMeetingszSz..zSzTGVzSzfinal1.pdf/burges98tutorial.pdf>`_

`Support Vector Machines for 3D Object Recognition <http://ieeexplore.ieee.org/iel4/34/15030/00683777.pdf?isnumber=15030&prod=JNL&arnumber=683777&arSt=637&ared=646&arAuthor=Pontil%2C+M.%3B+Verri%2C+A.>`_

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>

Purdue link: http://www2.lib.purdue.edu:2483/10.1145/130385.130401

ACM link: http://doi.acm.org/10.1145/130385.130401

  • 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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