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Sparse Representation for Gaussian Process Models

Sparse Representation for Gaussian Process Models,Lehel Csató,Manfred Opper

Sparse Representation for Gaussian Process Models   (Citations: 56)
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Abstract 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 releva nt subsample of the data which fully specifies the prediction of the model.,Experimental results on toy examples and large real-world dataset s indicate the efficiency of the approach.
Conference: Neural Information Processing Systems - NIPS , pp. 444-450, 2000
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