General Bounds on Bayes Errors for Regression with Gaussian Processes
Abstract
Based on a simple convexity lemma, we develop bounds for differ(cid:173) ent types of Bayesian prediction errors for regression with Gaussian processes. The basic bounds are formulated for a fixed training set. Simpler expressions are obtained for sampling from an input distri(cid:173) bution which equals the weight function of the covariance kernel, yielding asymptotically tight results. The results are compared with numerical experiments.
Cite
Text
Opper and Vivarelli. "General Bounds on Bayes Errors for Regression with Gaussian Processes." Neural Information Processing Systems, 1998.Markdown
[Opper and Vivarelli. "General Bounds on Bayes Errors for Regression with Gaussian Processes." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/opper1998neurips-general/)BibTeX
@inproceedings{opper1998neurips-general,
title = {{General Bounds on Bayes Errors for Regression with Gaussian Processes}},
author = {Opper, Manfred and Vivarelli, Francesco},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {302-308},
url = {https://mlanthology.org/neurips/1998/opper1998neurips-general/}
}