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@inproceedings{Snelson2007FlexibleAE, title={Flexible and efficient Gaussian process models for machine learning}, author={Edward Lloyd Snelson}, year={2007} }

- Published 2007

Gaussian process (GP) models are widely used to perform Bayesian nonlinear regression and classification — tasks that are central to many machine learning problems. A GP is nonparametric, meaning that the complexity of the model grows as more data points are received. Another attractive feature is the behaviour of the error bars. They naturally grow in regions away from training data where we have high uncertainty about the interpolating function. In their standard form GPs have several… CONTINUE READING

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