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Book Chapter

Approximation Methods for Gaussian Process Regression

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Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
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 L. Bottou, O. Chapelle, D. DeCoste, & J. Weston (Eds.), Large-Scale Kernel Machines (pp. 203-223). Cambridge, MA, USA: MIT Press.


Cite as: https://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.