A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying overview of sparse approximations, following Quiñonero-Candela and Rasmussen (2005), and a brief review of approximate matrix-vector multiplication methods.
| Author(s): | Quiñonero-Candela, J. and Rasmussen, CE. and Williams, CKI. |
| Book Title: | Large-Scale Kernel Machines |
| Pages: | 203-223 |
| Year: | 2007 |
| Month: | September |
| Day: | 0 |
| Series: | Neural Information Processing |
| Editors: | Bottou, L. , O. Chapelle, D. DeCoste, J. Weston |
| Publisher: | MIT Press |
| Bibtex Type: | Book Chapter (inbook) |
| Address: | Cambridge, MA, USA |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@inbook{4798,
title = {Approximation Methods for Gaussian Process Regression},
booktitle = {Large-Scale Kernel Machines},
abstract = {A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying overview of sparse approximations, following Quiñonero-Candela and Rasmussen (2005), and a brief review of approximate matrix-vector multiplication methods.},
pages = {203-223},
series = {Neural Information Processing},
editors = {Bottou, L. , O. Chapelle, D. DeCoste, J. Weston},
publisher = {MIT Press},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {Cambridge, MA, USA},
month = sep,
year = {2007},
slug = {4798},
author = {Quiñonero-Candela, J. and Rasmussen, CE. and Williams, CKI.},
month_numeric = {9}
}