Robust optimization in Support Vector Machine training with bounded errors

被引：0
|
作者：
Trafalis, TB [1 ]
Alwazzi, SA [1 ]
机构：
[1] Univ Oklahoma, Sch Ind Engn, Lab Optimizat & Intelligent Syst, Norman, OK 73019 USA
来源：
PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4 | 2003年
关键词：
robust optimization; kernel methods; support vector machines; semidefinite programming;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we investigate the stability of the linear programming Support Vector Machine (LP-SVM) solution under bounded perturbations of the input data using a robust optimization model. Preliminary experimental results are presented for toy and real world data.
引用
收藏
页码：2039 / 2042
页数：4
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