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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