Approximation Methods for Gaussian Process Regression
- Joaquin Quiñonero Candela
in Large Scale Learning Machines
Published by MIT Press | 2007 | Edition Large Scale Learning Machines
A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying overview of sparse approximations, following Qui˜nonero-Candela and Rasmussen (2005), and a brief review of approximate matrix-vector multiplication methods.