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Lectures discussing Support Vector Machines: [[Lecture 11 - Fischer's Linear Discriminant again_OldKiwi|Lecture 11]], [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|Lecture 12]] and [[Lecture 13 - Kernel function for SVMs and ANNs introduction_OldKiwi|Lecture 13]]. | Lectures discussing Support Vector Machines: [[Lecture 11 - Fischer's Linear Discriminant again_OldKiwi|Lecture 11]], [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|Lecture 12]] and [[Lecture 13 - Kernel function for SVMs and ANNs introduction_OldKiwi|Lecture 13]]. | ||
| + | Relevant Homework [[Homework 2_OldKiwi]] | ||
== Useful Links == | == Useful Links == | ||
Revision as of 16:53, 30 March 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_OldKiwi
Useful Links
- `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.
