Search from over 60,000 research works

Advanced Search

Estimation of Gaussian process regression model using probability distance measures

[thumbnail of Open Access]
Preview
21642583.2014.970731-1.pdf - Published Version (422kB) | Preview
Available under license: Creative Commons Attribution
Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Hong, X. orcid id iconORCID: https://orcid.org/0000-0002-6832-2298, 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 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.

Altmetric Badge

Item Type Article
URI https://reading-clone.eprints-hosting.org/id/eprint/39721
Item Type Article
Refereed Yes
Divisions Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Uncontrolled Keywords Gaussian process; optimization; probability distance measures
Publisher Taylor & Francis.
Download/View statistics View download statistics for this item

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Search Google Scholar