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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.