A prediction interval estimation method for KMSE

Authors:
Changha Hwang;Kyung Ha Seok;Daehyeon Cho
Affiliations:
Division of Information and Computer Sciences, Dankook University, Seoul, Korea;Corresponding Author, Department of Data Science, Inje University, Kyungnam, Korea;Department of Data Science, Inje University, Kyungnam, Korea
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

The kernel minimum squared error estimation (KMSE) model can be viewed as a general framework that includes kernel Fisher discriminant analysis (KFDA), least squares support vector machine (LS-SVM), and kernel ridge regression (KRR) as its particular cases. For continuous real output the equivalence of KMSE and LS-SVM is shown in this paper. We apply standard methods for computing prediction intervals in nonlinear regression to KMSE model. The simulation results show that LS-SVM has better performance in terms of the prediction intervals and mean squared error(MSE). The experiment on a real date set indicates that KMSE compares favorably with other method.