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.
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Hwang, C., Seok, K.H., Cho, D. (2005). A Prediction Interval Estimation Method for KMSE. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_69
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DOI: https://doi.org/10.1007/11539087_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
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