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.
@article{arxiv.1402.4861,
title = {A Quasi-Newton Method for Large Scale Support Vector Machines},
author = {Aryan Mokhtari and Alejandro Ribeiro},
journal= {arXiv preprint arXiv:1402.4861},
year = {2014}
}
Comments
5 pages, To appear in International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2014