Abstract
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.
Cite
CITATION STYLE
Malzahn, D., & Opper, M. (2001). Learning curves for Gaussian processes models: Fluctuations and Universality. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2130, pp. 271–276). Springer Verlag. https://doi.org/10.1007/3-540-44668-0_39
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