m |
|||
| (4 intermediate revisions by one other user not shown) | |||
| Line 1: | Line 1: | ||
| − | |||
| − | |||
This page and its subtopics discusses about Support Vector Machines | This page and its subtopics discusses about Support Vector Machines | ||
| Line 11: | Line 9: | ||
* [http://en.wikipedia.org/wiki/Support_vector_machine Support Vector Machine on Wikipedia] | * [http://en.wikipedia.org/wiki/Support_vector_machine Support Vector Machine on Wikipedia] | ||
| + | |||
| + | * [http://www.csie.ntu.edu.tw/~cjlin/libsvm/ LIBSVM ] - A library of SVM software, including both C and Matlab code. Various interfaces through several platforms available as well. | ||
* [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. | ||
| Line 23: | Line 23: | ||
* [http://doi.acm.org/10.1145/130385.130401 ACM link to SVM] | * [http://doi.acm.org/10.1145/130385.130401 ACM link to SVM] | ||
| + | |||
| + | * [http://asi.insa-rouen.fr/enseignants/~arakotom/toolbox/index.html SVM and Kernel Methods Matlab Toolbox] | ||
| + | |||
| + | * [http://www.support-vector-machines.org/SVM_soft.html SVM - Support Vector Machines Software] | ||
| + | |||
| + | * [http://www.cs.iastate.edu/~dcaragea/SVMVis/data_sets.htm Some SVM sample data ] | ||
| + | |||
| + | * [http://www.mathworks.com/access/helpdesk/help/toolbox/bioinfo/index.html?/access/helpdesk/help/toolbox/bioinfo/ref/svmclassify.html&http://www.mathworks.com/cgi-bin/texis/webinator/search/ SVM Matlab Bioinformatics Toolbox ] | ||
== Journal References == | == Journal References == | ||
| Line 29: | Line 37: | ||
* 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. | ||
| + | |||
| + | [[Category:ECE662]] | ||
Latest revision as of 08:48, 10 April 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_Old Kiwi
Useful Links
- LIBSVM - A library of SVM software, including both C and Matlab code. Various interfaces through several platforms available as well.
- 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.
