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

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

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Dörthe Malzahn, Manfred Opper


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.