Sparse Representation for Gaussian Process Models
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 al(cid:173) gorithm together with a sequential construction of a relevant subsample of the data which fully specifies the prediction of the model. Experi(cid:173) mental results on toy examples and large real-world data sets indicate the efficiency of the approach.
Cite
Text
Csató and Opper. "Sparse Representation for Gaussian Process Models." Neural Information Processing Systems, 2000.Markdown
[Csató and Opper. "Sparse Representation for Gaussian Process Models." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/csato2000neurips-sparse/)BibTeX
@inproceedings{csato2000neurips-sparse,
title = {{Sparse Representation for Gaussian Process Models}},
author = {Csató, Lehel and Opper, Manfred},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {444-450},
url = {https://mlanthology.org/neurips/2000/csato2000neurips-sparse/}
}