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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] | + | * [http://www2.lib.purdue.edu:2483/10.1145/130385.130401 Purdue link to SVM] |
| − | * [http://doi.acm.org/10.1145/130385.130401 | + | * [http://doi.acm.org/10.1145/130385.130401 ACM link to SVM] |
| − | ACM link to SVM] | + | |
| − | + | == 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. | + | * 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. | + | * 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
- `A Practical Guide to Support Vector Classification: Mainly created for beginners, it quickly explains how to use the libsvm.
- 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.
