DocumentCode
396664
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
Volume
3
fYear
2003
fDate
20-24 July 2003
Firstpage
2039
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-7898-9
Type
conf
DOI
10.1109/IJCNN.2003.1223721
Filename
1223721
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