PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

How to choose the covariance for Gaussian process regression independently of the basis
Matthias Franz and Peter Gehler
In: Gaussian Processes in Practice, Bletchley Park, UK(2006).

Abstract

In Gaussian process regression, both the basis functions and their prior distribution are simultaneously specified by the choice of the covariance function. In certain problems one would like to choose the covariance independently of the basis functions (e.g., in polynomial signal processing or Wiener and Volterra analysis). We propose a solution to this problem that approximates the desired covariance function at a finite set of input points for arbitrary choices of basis functions. Our experiments show that this additional degree of freedom can lead to improved regression performance.

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EPrint Type:Conference or Workshop Item (Spotlight)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:2319
Deposited By:Matthias Franz
Deposited On:19 November 2006