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Estimation of Gaussian process regression model using probability distance measures

Hong, X., Gao, J., Jiang, X. and Harris, C. J. (2014) Estimation of Gaussian process regression model using probability distance measures. Systems Science & Control Engineering, 2. pp. 655-663. ISSN 2164-2583

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To link to this item DOI: 10.1080/21642583.2014.970731

Abstract/Summary

A new class of parameter estimation algorithms is introduced for Gaussian process regression (GPR) models. It is shown that the integration of the GPR model with probability distance measures of (i) the integrated square error and (ii) Kullback–Leibler (K–L) divergence are analytically tractable. An efficient coordinate descent algorithm is proposed to iteratively estimate the kernel width using golden section search which includes a fast gradient descent algorithm as an inner loop to estimate the noise variance. Numerical examples are included to demonstrate the effectiveness of the new identification approaches.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:39721
Uncontrolled Keywords:Gaussian process; optimization; probability distance measures
Publisher:Taylor & Francis.

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