Learning curves for Gaussian processes via numerical cubature integration

Authors:
Simo Särkkä
Affiliations:
Department of Biomedical Engineering and Computational Science, Aalto University, Finland
Venue:
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Year:
2011

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Abstract

This paper is concerned with estimation of learning curves for Gaussian process regression with multidimensional numerical integration. We propose an approach where the recursion equations for the generalization error are approximately solved using numerical cubature integration methods. The advantage of the approach is that the eigenfunction expansion of the covariance function does not need to be known. The accuracy of the proposed method is compared to eigenfunction expansion based approximations to the learning curve.