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Approximation methods for gaussian process regression

Research output: Chapter in Book/Report/Conference proceedingChapter

Original languageEnglish
Title of host publicationLarge-Scale Kernel Machines
EditorsLéon Bottou, Olivier Chapelle, Dennis DeCoste , Jason Weston
PublisherMIT Press
Number of pages24
ISBN (Electronic)9780262250917
ISBN (Print)9780262026253
Publication statusPublished - Aug 2007

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 Qui˜nonero-Candela and Rasmussen
(2005), and a brief review of approximate matrix-vector multiplication
methods.

ID: 24654827