Learning curves for Gaussian processes
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning curves for Gaussian process regression: approximations and bounds
Neural Computation
Can gaussian process regression be made robust against model mismatch?
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
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Based on a statistical mechanics approach, we develop a method for approximately computing average case learning curves and their sample fluctuations for Gaussian process regression models.We give examples for the Wiener process and show that universal relations (that are independent of the input distribution) between error measures can be derived.