Fast Sparse Gaussian Process Classification With Multiple Classes

Abstract

The favourable scaling behaviour of sparse approximations to Bayesian inference for Gaussian Process models makes them attractive for large-scale applications. We show how to generalize the Informative Vector Machine (IVM) [3] to a multi-way classification setting. While being a kernel-based approach, our method yields valid uncertainty estimates and allows… (More)

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Cite this paper

@inproceedings{SeegerFastSG, title={Fast Sparse Gaussian Process Classification With Multiple Classes}, author={Matthias Seeger and Michael I. Jordan} }