Title :
Robust optimization in support vector machine training with bounded errors
Author :
Trafalis, Theodore B. ; Alwazzi, Samir A.
Author_Institution :
Sch. of Ind. Eng., Oklahoma Univ., Norman, OK, USA
Abstract :
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.
Keywords :
learning (artificial intelligence); linear programming; pattern classification; support vector machines; bounded error; bounded perturbation; kernel methods; learning; linear programming support vector machine; pattern classification; robust optimization model; semidefinite programming; support vector machine training; Industrial training; Laboratories; Least squares approximation; Mathematical model; Mathematical programming; Noise robustness; Robust stability; Support vector machine classification; Support vector machines; Uncertainty;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223721
