Node Relevance Determination

[edit]

Neil D. Lawrence, University of Sheffield

in The {XI}th Italian Workshop on Neural Networks

Related Material

Abstract

Hierarchical Bayesian inference in parameterised models offers an approach for controlling complexity. In this paper we utilise a novel prior for the leaning of a model’s structure. We call the prior node relevance determination. It is applicable in a range of models including sigmoid belief networks and Boltzmann machines. We demonstrate how the approach may be applied to determine structure in a multi-layer perceptron.


@InProceedings{lawrence-nrd01,
  title = 	 {Node Relevance Determination},
  author = 	 {Neil D. Lawrence},
  booktitle = 	 {The {XI}th Italian Workshop on Neural Networks},
  year = 	 {2001},
  editor = 	 {Maria Marinaro and Roberto Tagliaferri},
  month = 	 {00},
  organization = {IIASS ``Eduardo R. Caianiello''},
  publisher = 	 {Springer-Verlag},
  edit = 	 {https://github.com/lawrennd//publications/edit/gh-pages/_posts/2001-01-01-lawrence-nrd01.md},
  url =  	 {http://inverseprobability.com/publications/lawrence-nrd01.html},
  abstract = 	 {Hierarchical Bayesian inference in parameterised models offers an approach for controlling complexity. In this paper we utilise a novel prior for the leaning of a model’s structure. We call the prior *node relevance determination*. It is applicable in a range of models including sigmoid belief networks and Boltzmann machines. We demonstrate how the approach may be applied to determine structure in a multi-layer perceptron.},
  crossref =  {Marinaro:wirn01},
  key = 	 {Lawrence:nrd01},
  linkpdf = 	 {http://www.thelawrences.net/neil/structure.pdf},
  linkpsgz =  {http://www.thelawrences.net/neil/structure.ps.gz},
  OPTgroup = 	 {}
 

}
%T Node Relevance Determination
%A Neil D. Lawrence
%B 
%C The {XI}th Italian Workshop on Neural Networks
%D 
%E Maria Marinaro and Roberto Tagliaferri
%F lawrence-nrd01
%I Springer-Verlag	
%P --
%R 
%U http://inverseprobability.com/publications/lawrence-nrd01.html
%X Hierarchical Bayesian inference in parameterised models offers an approach for controlling complexity. In this paper we utilise a novel prior for the leaning of a model’s structure. We call the prior *node relevance determination*. It is applicable in a range of models including sigmoid belief networks and Boltzmann machines. We demonstrate how the approach may be applied to determine structure in a multi-layer perceptron.
TY  - CPAPER
TI  - Node Relevance Determination
AU  - Neil D. Lawrence
BT  - The {XI}th Italian Workshop on Neural Networks
PY  - 2001/01/01
DA  - 2001/01/01
ED  - Maria Marinaro
ED  - Roberto Tagliaferri	
ID  - lawrence-nrd01
PB  - Springer-Verlag	
SP  - 
EP  - 
L1  - http://www.thelawrences.net/neil/structure.pdf
UR  - http://inverseprobability.com/publications/lawrence-nrd01.html
AB  - Hierarchical Bayesian inference in parameterised models offers an approach for controlling complexity. In this paper we utilise a novel prior for the leaning of a model’s structure. We call the prior *node relevance determination*. It is applicable in a range of models including sigmoid belief networks and Boltzmann machines. We demonstrate how the approach may be applied to determine structure in a multi-layer perceptron.
ER  -

Lawrence, N.D.. (2001). Node Relevance Determination. The {XI}th Italian Workshop on Neural Networks :-