Gaussian Processes for regression : a tutorial

@inproceedings{Melo2012GaussianPF,
  title={Gaussian Processes for regression : a tutorial},
  author={Jos{\'e} Melo},
  year={2012}
}
Gaussian processes are a powerful, non-parametric tool that can be be used in supervised learning, namely in regression but also in classification problems. The main advantages of this method are the ability of GPs to provide uncertainty estimates and to learn the noise and smoothness parameters from training data. The aim of this short tutorial is to provide the basic theoretical aspects of Gaussian Processes, as well as a brief practical overview on implementation. The main motivation of this… CONTINUE READING
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