Using the Equivalent Kernel to Understand Gaussian Process Regression
Peter Sollich and Dr Christopher Williams
In: Neural Information Processing Systems 2004 (NIPS 2004), 14-16 Dec 2004, Vancouver, Canada.

Abstract

The equivalent kernel (Silverman, 1984) is a way of understanding how Gaussian process 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 kernels, and (2) how analysis using the equivalent kernel helps to understand the learning curves for Gaussian processes.

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