Upper and lower bounds on the learning curve for Gaussian processes


In this paper we introduce and illustrate non-trivial upper and lower bounds on the learning curves for one-dimensional Gaussian Processes. The analysis is carried out emphasising the effects induced on the bounds by the smoothness of the random process described by the Modified Bessel and the Squared Exponential covariance functions. We present an explanation of the early, linearly-decreasing behavior of the learning curves and the bounds as well as a study of the asymptotic behavior of the curves. The effects of the noise level and the lengthscale on the tightness of the bounds are also discussed.

Publication DOI: https://doi.org/10.1023/A:1007601601278
Divisions: Aston University (General)
Uncontrolled Keywords: non-trivial,Gaussian Processes,modified Bessel,covariance functions,learning curves,Artificial Intelligence,Control and Systems Engineering
Publication ISSN: 1573-0565
Last Modified: 12 Jun 2024 07:07
Date Deposited: 16 Sep 2009 15:35
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2000-07
Authors: Williams, Christopher K. I.
Vivarelli, Francesco



Version: Published Version

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