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Abstract: We present a framework for sparse Gaussian process (GP) methods which uses forward selection with criteria based on informationtheoretic principles, previously suggested for active learning. Our goal is not only to learn d--sparse predictors (which can be evaluated in O(d) rather than O(n), d n, n the number of training points), but also to perform training under strong restrictions on time and memory requirements. The scaling of our method is at most O(n ), and in large... (Update)
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BibTeX entry: (Update)
N. D. Lawrence, M. Seeger, and R. Herbrich. Fast sparse Gaussian process methods: The informative vector machine. In Becker et al. [1], pages 625--632. http://citeseer.ist.psu.edu/article/lawrence03fast.html More
@misc{ lawrence-fast,
author = "N. Lawrence and M. Seeger and R. Herbrich",
title = "Fast sparse Gaussian process methods: The informative vector machine",
text = "N. D. Lawrence, M. Seeger, and R. Herbrich. Fast sparse Gaussian process
methods: The informative vector machine. In Becker et al. [1], pages 625--632.",
url = "citeseer.ist.psu.edu/article/lawrence03fast.html" }
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63 California Institute of Technology (context) - MacKay, for et al. - 1991
50 Sparse Bayesian learning and the relevance vector machine - Tipping - 2001 ACM DBLP
41 A Family of Algorithms for Approximate Bayesian Inference - Minka - 2001 ACM
20 Sparse greedy Gaussian process regression - Smola, Bartlett - 2001
20 A Bayesian committee machine - Tresp - 2000 DBLP
15 Gaussian processes for classification: Mean field algorithms - Opper, Winther - 2000
10 Sparse online Gaussian processes (context) - Csato, Opper - 2002
5 A sparse Bayesian compression scheme - the informative vecto.. - Lawrence, Herbrich - 2001
4 Using the Nystrom method to speed up kernel machines (context) - Williams, Seeger - 2001
2 Sparse Bayesian learning: The informative vector machine (context) - Seeger, Lawrence et al. - 2002
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