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NIPS
2000

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Sparse Representation for Gaussian Process Models

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Sparse Representation for Gaussian Process Models

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We develop an approach for a sparse representation for Gaussian Process (GP) models in order to overcome the limitations of GPs caused by large data sets. The method is based on a combination of a Bayesian online algorithm together with a sequential construction of a relevant subsample of the data which fully specifies the prediction of the model. Experimental results on toy examples and large real-world datasets indicate the efficiency of the approach.

Lehel Csató, Manfred Opper

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Added	01 Nov 2010
Updated	01 Nov 2010
Type	Conference
Year	2000
Where	NIPS
Authors	Lehel Csató, Manfred Opper

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