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NIPS
2004

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Using the Equivalent Kernel to Understand Gaussian Process Regression

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Using the Equivalent Kernel to Understand Gaussian Process Regression

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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. Consider the supervised regression problem for a dataset D with entries (xi, yi) for i = 1, . . . , n. Under Gaussian Process (GP) assumptions the predictive mean at a test point x is given by

Peter Sollich, Christopher K. I. Williams

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Added	31 Oct 2010
Updated	31 Oct 2010
Type	Conference
Year	2004
Where	NIPS
Authors	Peter Sollich, Christopher K. I. Williams

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