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NIPS
2004

Learning Gaussian Process Kernels via Hierarchical Bayes

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Learning Gaussian Process Kernels via Hierarchical Bayes
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
Anton Schwaighofer, Volker Tresp, Kai Yu
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where NIPS
Authors Anton Schwaighofer, Volker Tresp, Kai Yu
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