Fast Sparse Gaussian Process Methods: The Informative Vector Machine

Neil D. Lawrence, Matthias Seeger, Ralf Herbrich
,  15:625-632, 2003.

Abstract

We present a framework for sparse Gaussian process (GP) methods which uses forward selection with criteria based on information-theoretical principles, previously suggested for active learning. In contrast to most previous work on sparse GPs, our goal is not only to learn sparse predictors (which can be evaluated in $O(d)$ rather than $O(n)$, $d<

Cite this Paper


BibTeX
@InProceedings{pmlr-v-lawrence-ivm02, title = {Fast Sparse Gaussian Process Methods: The Informative Vector Machine}, author = {Neil D. Lawrence and Matthias Seeger and Ralf Herbrich}, pages = {625--632}, year = {}, editor = {}, volume = {15}, address = {Cambridge, MA}, url = {http://inverseprobability.com/publications/lawrence-ivm02.html}, abstract = {We present a framework for sparse Gaussian process (GP) methods which uses forward selection with criteria based on information-theoretical principles, previously suggested for active learning. In contrast to most previous work on sparse GPs, our goal is not only to learn sparse predictors (which can be evaluated in $O(d)$ rather than $O(n)$, $d<
Endnote
%0 Conference Paper %T Fast Sparse Gaussian Process Methods: The Informative Vector Machine %A Neil D. Lawrence %A Matthias Seeger %A Ralf Herbrich %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-lawrence-ivm02 %I PMLR %J Proceedings of Machine Learning Research %P 625--632 %U http://inverseprobability.com %V %W PMLR %X We present a framework for sparse Gaussian process (GP) methods which uses forward selection with criteria based on information-theoretical principles, previously suggested for active learning. In contrast to most previous work on sparse GPs, our goal is not only to learn sparse predictors (which can be evaluated in $O(d)$ rather than $O(n)$, $d<
RIS
TY - CPAPER TI - Fast Sparse Gaussian Process Methods: The Informative Vector Machine AU - Neil D. Lawrence AU - Matthias Seeger AU - Ralf Herbrich BT - PY - DA - ED - ID - pmlr-v-lawrence-ivm02 PB - PMLR SP - 625 DP - PMLR EP - 632 L1 - UR - http://inverseprobability.com/publications/lawrence-ivm02.html AB - We present a framework for sparse Gaussian process (GP) methods which uses forward selection with criteria based on information-theoretical principles, previously suggested for active learning. In contrast to most previous work on sparse GPs, our goal is not only to learn sparse predictors (which can be evaluated in $O(d)$ rather than $O(n)$, $d<
APA
Lawrence, N.D., Seeger, M. & Herbrich, R.. (). Fast Sparse Gaussian Process Methods: The Informative Vector Machine. , in PMLR :625-632

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