期刊
ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE
卷 88, 期 1-3, 页码 269-289出版社
SPRINGER
DOI: 10.1007/s10472-019-09676-0
关键词
Robust classification; Random Fourier features; Nystrom method; Robust optimization; Support vector machines; Machine learning
We consider the binary classification problem when data are large and subject to unknown but bounded uncertainties. We address the problem by formulating the nonlinear support vector machine training problem with robust optimization. To do so, we analyze and propose two bounding schemes for uncertainties associated to random approximate features in low dimensional spaces. The proposed bound calculations are based on Random Fourier Features and the Nystrom methods. Numerical experiments are conducted to illustrate the benefit of the technique. We also emphasize the decomposable structure of the proposed robust nonlinear formulation that allows the use of efficient stochastic approximation techniques when datasets are large.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据