Data mining with parallel support vector machines for classification

Authors:
Tatjana Eitrich;Bruno Lang
Affiliations:
Research Centre Juelich, Central Institute for Applied Mathematics, Germany;Applied Computer Science and Scientific Computing Group, Department of Mathematics, University of Wuppertal, Germany
Venue:
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
Year:
2006

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Abstract

The increasing amount of data used for classification, as well as the demand for complex models with a large number of well tuned parameters, naturally lead to the search for efficient approaches making use of massively parallel systems. We describe the parallelization of support vector machine learning for shared memory systems. The support vector machine is a powerful and reliable data mining method. Our learning algorithm relies on a decomposition scheme, which in turn uses a special variable projection method, for solving the quadratic program associated with support vector machine learning. By using hybrid parallel programming, our parallelization approach can be combined with the parallelism of a distributed cross validation routine and parallel parameter optimization methods.