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JMLR
2006

Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems

11 years 8 months ago
Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
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-ofthe-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 mach...
Luca Zanni, Thomas Serafini, Gaetano Zanghirati
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where JMLR
Authors Luca Zanni, Thomas Serafini, Gaetano Zanghirati
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