Sciweavers

Share
CVPR
2008
IEEE

A Parallel Decomposition Solver for SVM: Distributed dual ascend using Fenchel Duality

10 years 9 months ago
A Parallel Decomposition Solver for SVM: Distributed dual ascend using Fenchel Duality
We introduce a distributed algorithm for solving large scale Support Vector Machines (SVM) problems. The algorithm divides the training set into a number of processing nodes each running independently an SVM sub-problem associated with its subset of training data. The algorithm is a parallel (Jacobi) block-update scheme derived from the convex conjugate (Fenchel Duality) form of the original SVM problem. Each update step consists of a modified SVM solver running in parallel over the sub-problems followed by a simple global update. We derive bounds on the number of updates showing that the number of iterations (independent SVM applications on sub-problems) required to obtain a solution of accuracy is O(log(1/ )). We demonstrate the efficiency and applicability of our algorithms by running on large scale experiments on standardized datasets while comparing the results to the state-of-the-art SVM solvers.
Tamir Hazan, Amit Man, Amnon Shashua
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2008
Where CVPR
Authors Tamir Hazan, Amit Man, Amnon Shashua
Comments (0)
books