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Abstract: Non-parametric models and techniques enjoy a growing popularity in the field of machine learning, and among these Bayesian inference for Gaussian process (GP) models has recently received significant attention. We feel that GP priors should be part of the standard toolbox for constructing models relevant to machine learning in the same way as parametric linear models are, and the results in this thesis help to remove some obstacles on the way towards this goal. (Update)
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Sparse Gaussian Process Classification with Multiple Classes - Seeger, Jordan (2004) (Correct)
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BibTeX entry: (Update)
M. Seeger. Bayesian Gaussian Process Models: PAC-Bayesian Generalisation Error Bounds and Sparse Approximations. PhD thesis, University of Edinburgh, July 2003. See www.cs.berkeley.edu/~mseeger. http://citeseer.ist.psu.edu/seeger03bayesian.html More
@misc{ seeger03bayesian,
author = "M. Seeger",
title = "Bayesian Gaussian Process Models: PAC-Bayesian Generalisation Error Bounds
and Sparse Approximations",
text = "M. Seeger. Bayesian Gaussian Process Models: PAC-Bayesian Generalisation
Error Bounds and Sparse Approximations. PhD thesis, University of Edinburgh,
July 2003. See www.cs.berkeley.edu/~mseeger.",
year = "2003",
url = "citeseer.ist.psu.edu/seeger03bayesian.html" }
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