PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Approximation Methods for Gaussian Process Regression
Joaquin Quinonero Candela, Carl Edward Rasmussen and Christopher Williams
In: Large Scale Kernel Machines Adaptive Computation and Machine Learning . (2006) The MIT Press , Cambridge, MA .

Abstract

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

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2666
Deposited By:Joaquin Quinonero Candela
Deposited On:22 November 2006