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 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.
