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A Quasi-Newton Method for Large Scale Support Vector Machines
This paper adapts a recently developed regularized stochastic version of the
Broyden, Fletcher, Goldfarb, and Shanno (BFGS) quasi-Newton method for the
solution of support vector machine classification problems. The proposed method
is shown to converge almost surely to the optimal classifier at a rate that is
linear in expectation. Numerical results show that the proposed method exhibits
a convergence rate that degrades smoothly with the dimensionality of the
feature vectors.Comment: 5 pages, To appear in International Conference on Acoustics, Speech,
and Signal Processing (ICASSP) 201
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