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2009

Support Vector Machines in Machine Learning

by

Hans D. Mittelmann
(Department of Math & Stats, Arizona State University)

on

January 06, 2009
(Tuesday)

at

02:30 ? 03:30 PM
L H ? 1, Department of Mathematics
Indian Institute of Science, Bangalore

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 SVM light algorithm, the SVM-QP method, andthe 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.


 

 

 


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