How to choose the covariance for Gaussian process regression independently of the basis
author: Matthias O. Franz,
Max Planck Institute for Biological Cybernetics, Max Planck Institute
published: Feb. 25, 2007, recorded: June 2006, views: 6051
published: Feb. 25, 2007, recorded: June 2006, views: 6051
Related content
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Description
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.
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Write your own review or comment: