PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Parallel software for training large scale support vector machines on multiprocessor systems
Luca Zanni, Thomas Serafini and Gaetano Zanghirati
Journal of Machine Learning Research Volume 7, pp. 1467-1492, 2006. ISSN 1532-4435

Abstract

A parallel software for solving the quadratic program arising in training support vector machines for classification problems is introduced. The software implements an iterative decomposition technique and exploits both the storage and the computing resources available on multiprocessor systems, by distributing the heaviest computational tasks of each decomposition iteration. Based on a wide range of recent theoretical advances, relevant decomposition issues, such as the quadratic subproblem solution, the gradient updating, the working set selection, are systematically described and their careful combination to get an effective parallel tool is discussed. A comparison with state-of-the-art packages on benchmark problems demonstrates the good accuracy and the remarkable time saving achieved by the proposed software. Furthermore, challenging experiments on real-world data sets with millions training samples highlight how the software makes large-scale standard nonlinear support vector machines effectively tractable on common multiprocessor systems. This feature is not shown by any of the available codes.

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EPrint Type:Article
Additional Information:PGPDT software homepage: http://dm.unife.it/gpdt
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3366
Deposited By:Gaetano Zanghirati
Deposited On:09 February 2008