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This page and its subtopics discusses about Support Vector Machines
 
This page and its subtopics discusses about Support Vector Machines
  
Lectures discussing Support Vector Machines: [[Lecture 11 - Fischer's Linear Discriminant again_Old Kiwi|Lecture11]], [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|Lecture12]] and [[Lecture 13 - Kernel function for SVMs and ANNs introduction_Old Kiwi|Lecture13]]
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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]].
  
* Other related  sites:
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Relevant Homework [[Homework 2_Old Kiwi]]
  
* http://en.wikipedia.org/wiki/Support_vector_machine
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== Useful Links ==
  
`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>`_
 
  
`Support Vector Machines for 3D Object Recognition
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* [http://en.wikipedia.org/wiki/Support_vector_machine Support Vector Machine on Wikipedia]
<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.>`_
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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.
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* [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.svms.org/software.html>
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Purdue link: http://www2.lib.purdue.edu:2483/10.1145/130385.130401
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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.
  
ACM link: http://doi.acm.org/10.1145/130385.130401
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* [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]
  
* Journal References
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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]
  
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.
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* [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.
  
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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* [http://www2.lib.purdue.edu:2483/10.1145/130385.130401 Purdue link to SVM]
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* [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 ]
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== Journal References ==
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* 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.
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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.
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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.
  • 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.

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