Line 5: Line 5:
 
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 ==

Revision as of 17:53, 30 March 2008

Course Topics

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

  • 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.

Alumni Liaison

Meet a recent graduate heading to Sweden for a Postdoctorate.

Christine Berkesch