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.

Divisions: Aston University (General)
Publication ISSN: 1573-0565
Last Modified: 29 Nov 2023 10:01
Date Deposited: 16 Sep 2009 15:35
Full Text Link: 10.1023/A:1007601601278
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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