A prediction interval estimation method for KMSE

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. © Springer-Verlag Berlin Heidelberg 2005.