Covariance functions and Bayes errors for GP regression on random graphs
published: Oct. 9, 2008, recorded: September 2008, views: 3790
Report a problem or upload filesIf 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.
We consider GP learning of functions defined on the nodes of a random graph. Covariance functions proposed for this scenario, based on diffusion processes on the graph, are shown to have some counter-intuitive properties. In particular, on graphs with tree-like structure where loops can be neglected (as is typically the case for randomly generated graphs), the "obvious" limit of a large correlation length scale does not produce a constant covariance function. In the second part, we look at Bayes errors for GP regression on graphs and study how the learning curves depend on the size of the graph, its connectivity, and the number of training examples.
Joint work with Camille Coti.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !