Decomposition Algorithms for Training Large-scale Semiparametric Support Vector Machines

author: Sangkyun Lee, University of Wisconsin - Madison
published: Oct. 20, 2009,   recorded: September 2009,   views: 2523

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We describe a method for solving large-scale semiparametric support vector machines (SVMs) for regression problems. Most of the approaches proposed to date for large-scale SVMs cannot accommodate the multiple equality constraints that appear in semiparametric problems. Our approach uses a decomposition framework, with a primal-dual algorithm to find an approximate saddle point for the min-max formulation of each subproblem. We compare our method with algorithms previously proposed for semiparametric SVMs, and show that it scales well as the number of training examples grows.

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