A QUASI-NEWTON METHOD FOR LARGE SCALE SUPPORT VECTOR MACHINES

被引：0

作者：

Mokhtari, Aryan ^{[1
]}

Ribeiro, Alejandro ^{[1
]}

机构：

[1] Univ Penn, Dept Elect & Syst Engn, Philadelphia, PA 19104 USA

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2014年

关键词：

CONVERGENCE;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页数：5

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