Approximation Methods for Gaussian Process Regression
Joaquin Quiñonero Candela, Carl Edward Rasmussen, and Christopher K. I. Williams
September 2007
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.
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Publisher: MIT Press
All copyrights reserved by MIT Press 2007.
Details
| Type: | TechReport |
| URL: | http://www.mitpress.mit.edu/ |
| Number: | MSR-TR-2007-124 |
| Pages: | 23 |
| Institution: | Microsoft Research |
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