Using the Equivalent Kernel to Understand Gaussian Process Regression
Abstract
The equivalent kernel [1] is a way of understanding how Gaussian pro- cess regression works for large sample sizes based on a continuum limit. In this paper we show (1) how to approximate the equivalent kernel of the widely-used squared exponential (or Gaussian) kernel and related ker- nels, and (2) how analysis using the equivalent kernel helps to understand the learning curves for Gaussian processes.
Cite
Text
Sollich and Williams. "Using the Equivalent Kernel to Understand Gaussian Process Regression." Neural Information Processing Systems, 2004.Markdown
[Sollich and Williams. "Using the Equivalent Kernel to Understand Gaussian Process Regression." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/sollich2004neurips-using/)BibTeX
@inproceedings{sollich2004neurips-using,
title = {{Using the Equivalent Kernel to Understand Gaussian Process Regression}},
author = {Sollich, Peter and Williams, Christopher},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1313-1320},
url = {https://mlanthology.org/neurips/2004/sollich2004neurips-using/}
}