Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations
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.
Cite
Text
Malzahn and Opper. "Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations." Neural Information Processing Systems, 2000.Markdown
[Malzahn and Opper. "Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/malzahn2000neurips-learning/)BibTeX
@inproceedings{malzahn2000neurips-learning,
title = {{Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations}},
author = {Malzahn, Dörthe and Opper, Manfred},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {273-279},
url = {https://mlanthology.org/neurips/2000/malzahn2000neurips-learning/}
}