Learning Gaussian Process Kernels via Hierarchical Bayes

@inproceedings{Schwaighofer2004LearningGP,
  title={Learning Gaussian Process Kernels via Hierarchical Bayes},
  author={Anton Schwaighofer and Volker Tresp and Kai Yu},
  booktitle={NIPS},
  year={2004}
}
We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are learned from data using a simple and efficient EM algorithm. This step is nonparametric, in that it does not require a parametric form of covariance function. In a second step, kernel functions are fitted to approximate the learned covariance matrix using a generalized Nystr öm method, which results in a complex, data… CONTINUE READING
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