nips nips2003 nips2003-194 nips2003-194-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Edward Snelson, Zoubin Ghahramani, Carl E. Rasmussen
Abstract: We generalise the Gaussian process (GP) framework for regression by learning a nonlinear transformation of the GP outputs. This allows for non-Gaussian processes and non-Gaussian noise. The learning algorithm chooses a nonlinear transformation such that transformed data is well-modelled by a GP. This can be seen as including a preprocessing transformation as an integral part of the probabilistic modelling problem, rather than as an ad-hoc step. We demonstrate on several real regression problems that learning the transformation can lead to significantly better performance than using a regular GP, or a GP with a fixed transformation. 1
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