Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations

Part of Advances in Neural Information Processing Systems 13 (NIPS 2000)

Authors

Dörthe Malzahn, Manfred Opper

Abstract

Based on a statistical mechanics approach, we develop a method for approximately computing average case learning curves for Gaus(cid:173) sian process regression models. The approximation works well in the large sample size limit and for arbitrary dimensionality of the input space. We explain how the approximation can be systemati(cid:173) cally improved and argue that similar techniques can be applied to general likelihood models.