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[[Category:ECE662]] | [[Category:ECE662]] | ||
| − | =Support Vector Machines= | + | =Support Vector Machines (SVM)= |
| + | ==Lecture Notes on SVM== | ||
| + | *Spring 2008, Prof. Boutin: Lecture [[Lecture_11_-_Fischer's_Linear_Discriminant_again_OldKiwi|11]],[[Lecture_12_-_Support_Vector_Machine_and_Quadratic_Optimization_Problem_OldKiwi|12]],[[Lecture_13_-_Kernel_function_for_SVMs_and_ANNs_introduction_OldKiwi|13]] | ||
| − | + | == Relevant Homework == | |
| + | *[[Homework 2_OldKiwi|HW2, Spring 2008, Prof. Boutin]] | ||
| + | == Useful Links == | ||
| + | * [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] | ||
| + | |||
| + | *[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://www2.lib.purdue.edu:2483/10.1145/130385.130401 Purdue 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 == | ||
| + | |||
| + | * 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. | ||
| + | |||
| + | |||
| + | |||
| + | |||
| + | ---- | ||
[[ ECE662|Back to ECE662]] | [[ ECE662|Back to ECE662]] | ||
Revision as of 10:33, 13 April 2010
Contents
Support Vector Machines (SVM)
Lecture Notes on SVM
Relevant Homework
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.
