**Monday**,
May 1, 2006, 3:40 p.m. PSA 113

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Dept Math & Stats

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Support Vector Machines in Machine Learning

slides

**Abstract**
Very large datasets occur in the area of machine learning (ML).
The tasks are having a computer "learn" to read handwriting,
to understand speech, to recognize faces, to filter spam
e-mail etc. Mathematically,
these problems lead to huge optimization problems, of an, however
not unfavorable type, namely convex quadratic programs (QP).
The talk starts with an introduction to the Support Vector
Machine methods for ML and their mathematical characteristics.
Three specific such algorithms are described, the SVMlight
algorithm, the SVM-QP method, and the Core-SVM approximation
method. A comparison on several large datasets is given.
Future work will concentrate on the SVM-QP method in
collaboration with its author Katya Scheinberg, IBM Watson
Research Center.

For further information please contact:
Anne Gelb