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Abstract: Gaussian process regression allows a simple analytical treatment of exact Bayesian inference and has been found to provide good performance, yet scales badly with the number of training data. In this paper we compare several approaches towards scaling Gaussian processes regression to large data sets: the subset of representers method, the reduced rank approximation, online Gaussian processes, and the Bayesian committee machine. Furthermore we provide theoretical insight into some of our... (Update)
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BibTeX entry: (Update)
Anton Schwaighofer and Volker Tresp. Transductive and Inductive Methods for Approximate Gaussian Process Regression. NIPS 2002. http://citeseer.ist.psu.edu/schwaighofer02transductive.html More
@misc{ schwaighofer02transductive,
author = "A. Schwaighofer and V. Tresp",
title = "Transductive and Inductive Methods for Approximate Gaussian Process Regression",
text = "Anton Schwaighofer and Volker Tresp. Transductive and Inductive Methods
for Approximate Gaussian Process Regression. NIPS 2002.",
year = "2002",
url = "citeseer.ist.psu.edu/schwaighofer02transductive.html" }
Citations (may not include all citations):1291 The nature of statistical learning theory (context) - Vapnik - 1995
696 UCI repository of machine learning databases (context) - Blake, Merz - 1998
47 Sparse greedy matrix approximation for machine learning - Smola, Sch - 2000
43 Using the nystr om method to speed up kernel machines (context) - Williams, Seeger
32 Introduction to Gaussian processes (context) - MacKay - 1998
20 Sparse greedy gaussian process regression - Smola, Bartlett
20 A Bayesian committee machine - Tresp - 2000
10 Sparse online gaussian processes (context) - Csat, Opper - 2002
8 Advances in Neural Information Processing Systems (context) - Leen, Dietterich et al. - 2001
5 The generalized bayesian committee machine - Tresp - 2000
4 Observations on the Nystr om method for Gaussian process pre.. (context) - Williams, Rasmussen et al. - 2002
2 Reduced rank Gaussian process learning (context) - Rasmussen - 2002
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