Using the equivalent kernel to understand Gaussian process regression

Peter Sollich, Christopher K.I. Williams

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The equivalent kernel [1] 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.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 17 (NIPS 2004)
Number of pages8
Publication statusPublished - 2004


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