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Lectures discussing Support Vector Machines: [[Lecture 11 - Fischer's Linear Discriminant again_Old Kiwi|Lecture 11]], [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|Lecture 12]] and [[Lecture 13 - Kernel function for SVMs and ANNs introduction_Old Kiwi|Lecture 13]]. | Lectures discussing Support Vector Machines: [[Lecture 11 - Fischer's Linear Discriminant again_Old Kiwi|Lecture 11]], [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|Lecture 12]] and [[Lecture 13 - Kernel function for SVMs and ANNs introduction_Old Kiwi|Lecture 13]]. | ||
| + | Relevant Homework [[Homework 2_Old Kiwi]] | ||
== Useful Links == | == Useful Links == | ||
| − | * 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/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://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] |
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| + | *[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. | ||
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* [http://doi.acm.org/10.1145/130385.130401 ACM link to SVM] | * [http://doi.acm.org/10.1145/130385.130401 ACM link to SVM] | ||
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| + | * [http://asi.insa-rouen.fr/enseignants/~arakotom/toolbox/index.html SVM and Kernel Methods Matlab Toolbox] | ||
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| + | * [http://www.support-vector-machines.org/SVM_soft.html SVM - Support Vector Machines Software] | ||
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| + | * [http://www.cs.iastate.edu/~dcaragea/SVMVis/data_sets.htm Some SVM sample data ] | ||
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| + | * [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 == | ||
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* 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. | ||
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| + | [[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.
