PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Understanding Gaussian Process Regression Using the Equivalent Kernel
Peter Sollich and Christopher Williams
In: Lecture notes in computer science (2005) Springer .

Abstract

The equivalent kernel is a way of understanding how Gaussian process regression works for large sample sizes based on a continuum limit. In this paper we show how to approximate the equivalent kernel of the widely-used squared exponential (or Gaussian) kernel and related kernels. This is easiest for uniform input densities, but we also discuss the generalization to the non-uniform case. We show further that the equivalent kernel can be used to understand the learning curves for Gaussian processes, and investigate how kernel smoothing using the equivalent kernel compares to full Gaussian process regression.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
Postscript - Requires a viewer, such as GhostView
EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:958
Deposited By:Peter Sollich
Deposited On:07 March 2005