期刊
APPLIED MATHEMATICS AND COMPUTATION
卷 171, 期 2, 页码 1264-1281出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.amc.2005.01.113
关键词
Gaussian process; log likelihood maximization; conjugate gradient; trust region; Hessian matrix
Gaussian process (GP) regression is a Bayesian non-parametric regression model, showing good performance in various applications. However, it is quite rare to see research results on log-likelihood maximization algorithms. Instead of the commonly used conjugate gradient method, the Hessian matrix is first derived/simplified in this paper and the trust-region optimization method is then presented to estimate GP hyper-parameters. Numerical experiments verify the theoretical analysis, showing the advantages of using Hessian matrix and trust-region algorithms. In the GP context, the trust-region optimization method is a robust alternative to conjugate gradient method, also in view of future researches on approximate and/or parallel GP-implementation. (c) 2005 Elsevier Inc. All rights reserved.
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