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2018 | OriginalPaper | Chapter

Using Gaussian Process to Enhance Support Vector Regression

Authors : Yi Zhang, Wen Yao, Xiaoqian Chen, Fred van Keulen

Published in: Advances in Structural and Multidisciplinary Optimization

Publisher: Springer International Publishing

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Abstract

Support vector regression (SVR) is a common surrogate model for computationally expensive simulation. It is able to balance the model complexity and the error tolerance. Whether SVR interpolates the training samples is dependent on its parameters. For the nonlinear function approximation without noise, when SVR is not an interpolator, it is advisable to model the errors and use them to compensate the prediction response. In this paper, the errors of SVR are modeled by using Gaussian process, and the final model response is obtained by the combination of SVR and the Gaussian process of the errors. The numerical experiments show the proposed method is able to further improve the prediction accuracy of SVR.

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Literature
1.
go back to reference Clarke, S.M., Griebsch, J.H., Simpson, T.W.: Analysis of support vector regression for approximation of complex engineering analyses. J. Mech. Des. 127(6), 1077–1087 (2005)CrossRef Clarke, S.M., Griebsch, J.H., Simpson, T.W.: Analysis of support vector regression for approximation of complex engineering analyses. J. Mech. Des. 127(6), 1077–1087 (2005)CrossRef
2.
go back to reference Forrester, A., Sobester, A., Keane, A.: Engineering Design via Surrogate Modelling: A Practical Guide. Wiley, Chichester (2008)CrossRef Forrester, A., Sobester, A., Keane, A.: Engineering Design via Surrogate Modelling: A Practical Guide. Wiley, Chichester (2008)CrossRef
3.
go back to reference Friedman, J.H.: Multivariate adaptive regression splines, The annals of statistics, 1991 Friedman, J.H.: Multivariate adaptive regression splines, The annals of statistics, 1991
4.
go back to reference Hombal, V., Mahadevan, S.: Model selection among physics-based models. J. Mech. Des. 135(2), 021003 (2013)CrossRef Hombal, V., Mahadevan, S.: Model selection among physics-based models. J. Mech. Des. 135(2), 021003 (2013)CrossRef
5.
go back to reference Jin, R., Chen, W., Simpson, T.W.: Comparative studies of metamodelling techniques under multiple modelling criteria. Struct. Multidiscip. Optim. 23(1), 1–13 (2001)CrossRef Jin, R., Chen, W., Simpson, T.W.: Comparative studies of metamodelling techniques under multiple modelling criteria. Struct. Multidiscip. Optim. 23(1), 1–13 (2001)CrossRef
6.
go back to reference Martin, J.D., Simpson, T.W.: Use of kriging models to approximate deterministic computer models. AIAA J. 43(4), 853–863 (2005)CrossRef Martin, J.D., Simpson, T.W.: Use of kriging models to approximate deterministic computer models. AIAA J. 43(4), 853–863 (2005)CrossRef
7.
8.
go back to reference Williams, C.K., Rasmussen, C.E.: Gaussian Processes for Machine Learning, vol. 2, No. 3, p. 4. the MIT Press (2006) Williams, C.K., Rasmussen, C.E.: Gaussian Processes for Machine Learning, vol. 2, No. 3, p. 4. the MIT Press (2006)
9.
go back to reference Yao, W., Chen, X., Zhao, Y., van Tooren, M.: Concurrent subspace width optimization method for RBF neural network modeling. IEEE Trans. Neural Netw. Learn. Syst. 23(2), 247–259 (2012)CrossRef Yao, W., Chen, X., Zhao, Y., van Tooren, M.: Concurrent subspace width optimization method for RBF neural network modeling. IEEE Trans. Neural Netw. Learn. Syst. 23(2), 247–259 (2012)CrossRef
Metadata
Title
Using Gaussian Process to Enhance Support Vector Regression
Authors
Yi Zhang
Wen Yao
Xiaoqian Chen
Fred van Keulen
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-319-67988-4_20

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