Robust kernel-based regression

Authors:
Budi Santosa;Theodore B. Trafalis
Affiliations:
Department of Industrial Engineering, Sepuluh Nopember Institute of Technology, Surabaya, Indonesia;School of Industrial Engineering, University of Oklahoma, Norman, OK
Venue:
CIMMACS'05 Proceedings of the 4th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Year:
2005

Citing 8
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Abstract

In this research, a robust optimization approach applied to support vector regression (SVR) is investigated. A novel kernel based-method is developed to address the problem of data uncertainty where each data point is inside a sphere. The model is called robust SVR. Computational results show that the resulting robust SVR model is better than traditional SVR in terms of robustness and generalization error.