HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Csató, L. Articles by Opper, M. Search for Related Content PubMed PubMed Citation Articles by Csató, L. Articles by Opper, M.

Sparse On-Line Gaussian Processes

csatol{at}aston.ac.uk, Neural Computing Research Group, Department of Information Engineering, Aston University, B4 7ET Birmingham, U. K.

Manfred Opper

opperm{at}aston.ac.uks, Neural Computing Research Group, Department of Information Engineering, Aston University, B4 7ET Birmingham, U. K.

We develop an approach for sparse representations of gaussian process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method is based on a combination of a Bayesian on-line algorithm, together with a sequential construction of a relevant subsample of the data that fully specifies the prediction of the GP model. By using an appealing parameterization and projection techniques in a reproducing kernel Hilbert space, recursions for the effective parameters and a sparse gaussian approximation of the posterior process are obtained. This allows for both a propagation of predictions and Bayesian error measures. The significance and robustness of our approach are demonstrated on a variety of experiments.

This article has been cited by other articles:

				M. Girolami and S. Rogers Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors Neural Comput., August 1, 2006; 18(8): 1790 - 1817. [Abstract] [Full Text] [PDF]

				S. Sundararajan, S. Shevade, and S. S. Keerthi Fast Generalized Cross-Validation Algorithm for Sparse Model Learning Neural Comput., January 1, 2006; 19(1): 283 - 301. [Abstract] [Full Text] [PDF]

				Z. Chen and S. Haykin On Different Facets of Regularization Theory Neural Comput., December 1, 2002; 14(12): 2791 - 2846. [Abstract] [Full Text]

				T. Zhang Approximation Bounds for Some Sparse Kernel Regression Algorithms Neural Comput., December 1, 2002; 14(12): 3013 - 3042. [Abstract] [Full Text]