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| − | * http://en.wikipedia.org/wiki/Support_vector_machine | + | * [http://en.wikipedia.org/wiki/Support_vector_machine Support Vector Machine on Wikipedia] |
| − | * | + | * [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://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] |
* [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. | ||
Revision as of 16:54, 30 March 2008
This page and its subtopics discusses about Support Vector Machines
Lectures discussing Support Vector Machines: Lecture 11, Lecture 12 and Lecture 13.
Relevant Homework Homework 2_OldKiwi
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.
