This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 400
Type: Topic Contributed
Date/Time: Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #307140
Title: Fast Stochastic Frank-Wolfe Algorithms for Nonlinear SVMs
Author(s): Hua Ouyang*+ and Alexander Gray
Companies: Georgia Institute of Technology and Georgia Institute of Technology
Address: 1305 KACB, Georgia Tech, Atlanta, GA, 30332,
Keywords: Stochastic Programming ; Support Vector Machines ; Online Learning ; Large Scale Problems ; Frank-Wolfe Algorithm
Abstract:

The high computational cost of nonlinear support vector machines has limited their usability for large-scale problems. We propose two novel stochastic algorithms to tackle this problem. These algorithms are based on a classic optimization method: the Frank-Wolfe method, which is known to be fast for problems with a large number of linear constraints. Formulating the nonlinear SVM problem to take advantage of this method, we achieve a provable time complexity of O(dQ^2/epsilon^2). The proposed algorithms achieve comparable or even better accuracies than the state-of-the-art methods, and are significantly faster.


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