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`[http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf A Practical Guide to Support Vector Classification]: Mainly created for beginners, it quickly explains how to use the libsvm. | `[http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf A Practical Guide to Support Vector Classification]: Mainly created for beginners, it quickly explains how to use the libsvm. | ||
| − | `[http://citeseer.ist.psu.edu/cache/papers/cs/26235/http:zSzzSzwww.isi.uu.nlzSzMeetingszSz..zSzTGVzSzfinal1.pdf/burges98tutorial.pdf 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 A Tutorial on Support Vector Machines for Pattern 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. Support Vector Machines for 3D Object Recognition] |
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| − | 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. |
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Purdue link: http://www2.lib.purdue.edu:2483/10.1145/130385.130401 | Purdue link: http://www2.lib.purdue.edu:2483/10.1145/130385.130401 | ||
Revision as of 08:30, 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.
- 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
`Support Vector Machines for 3D Object Recognition
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.
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.
