A Gaussian Belief Propagation Solver for Large Scale Support Vector Machines

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dc.creatorBickson, Danny
dc.creatorYom-Tov, Elad
dc.creatorDolev, Danny
dc.date2008-10-09
dc.date.accessioned2026-07-07T10:18:10Z
dc.date.available2026-07-07T10:18:10Z
dc.descriptionSupport vector machines (SVMs) are an extremely successful type of classification and regression algorithms. Building an SVM entails solving a constrained convex quadratic programming problem, which is quadratic in the number of training samples. We introduce an efficient parallel implementation of an support vector regression solver, based on the Gaussian Belief Propagation algorithm (GaBP). In this paper, we demonstrate that methods from the complex system domain could be utilized for performing efficient distributed computation. We compare the proposed algorithm to previously proposed distributed and single-node SVM solvers. Our comparison shows that the proposed algorithm is just as accurate as these solvers, while being significantly faster, especially for large datasets. We demonstrate scalability of the proposed algorithm to up to 1,024 computing nodes and hundreds of thousands of data points using an IBM Blue Gene supercomputer. As far as we know, our work is the largest parallel implementation of belief propagation ever done, demonstrating the applicability of this algorithm for large scale distributed computing systems.
dc.description12 pages, 1 figure, appeared in the 5th European Complex Systems Conference, Jerusalem, Sept. 2008
dc.identifierhttps://arxiv.org/abs/0810.1648
dc.identifierhttp://arxiv.org/abs/0810.1648
dc.identifierThe 5th European Complex Systems Conference (ECCS 2008), Jerusalem, Sept. 2008
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/174103
dc.subjectMachine Learning
dc.subjectInformation Theory
dc.subjectI.2.6
dc.titleA Gaussian Belief Propagation Solver for Large Scale Support Vector Machines
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