Approximation Methods for Gaussian Process Regression

Joaquin QuiƱonero Candela, Carl Edward Rasmussen, and Christopher K. I. Williams

Abstract

A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying overview of sparse approximations, following QuiƱonero-Candela and Rasmussen (2005), and a brief review of approximate matrix-vector multiplication methods.

Details

Publication typeTechReport
URLhttp://www.mitpress.mit.edu/
NumberMSR-TR-2007-124
Pages23
InstitutionMicrosoft Research
PublisherMIT Press
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