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Book Chapter

Approximation Methods for Gaussian Process Regression

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83845

Quiñonero-Candela,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84156

Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Quiñonero-Candela, J., Rasmussen, C., & Williams, C. (2007). Approximation Methods for Gaussian Process Regression. In Large-Scale Kernel Machines (pp. 203-223). Cambridge, MA, USA: MIT Press.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CBFB-A
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.