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