| Line 3: | Line 3: | ||
=Support Vector Machines (SVM)= | =Support Vector Machines (SVM)= | ||
| + | == Lectures on SVM== | ||
| + | *ECE662, Spring 2010, [[User:mboutin|Prof. Boutin]]: Lecture [[Lecture22ECE662S10|22]], [[Lecture23ECE662S10|23]] | ||
| + | *ECE662, Spring 2008, [[User:mboutin|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]] | ||
==Lecture Notes on 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]] | + | *ECE662, 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 == | == Relevant Homework == | ||
| − | *[[Homework 2_OldKiwi|HW2, Spring 2008, Prof. Boutin]] | + | *[[Homework 2_OldKiwi|HW2, ECE662, Spring 2008, Prof. Boutin]] |
| − | + | ||
== Useful Links == | == Useful Links == | ||
| − | |||
* [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] | ||
Latest revision as of 10:56, 13 April 2010
Contents
Support Vector Machines (SVM)
Lectures on SVM
- ECE662, Spring 2010, Prof. Boutin: Lecture 22, 23
- ECE662, Spring 2008, Prof. Boutin: Lecture 11,12,13
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.
