@inproceedings{cdabed3fc8ef42beaebf62de90c0ae87,
title = "A parallel decomposition solver for SVM: Distributed dual ascend using fenchel duality",
abstract = "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 ε is O(log(1/ε)). 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.",
author = "Tamir Hazan and Amit Man and Amnon Shashua",
year = "2008",
doi = "10.1109/CVPR.2008.4587354",
language = "American English",
isbn = "9781424422432",
series = "26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR",
booktitle = "26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR",
note = "26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR ; Conference date: 23-06-2008 Through 28-06-2008",
}