Node Relevance Determination

Neil D. Lawrence

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Node Relevance Determination

Neil D. Lawrence

The {XI}th Italian Workshop on Neural Networks, Springer-Verlag , 2001.

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.

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BibTeX


@InProceedings{Lawrence:nrd01,
  title = 	 {Node Relevance Determination},
  author = 	 {Lawrence, Neil D.},
  booktitle = 	 {The {XI}th Italian Workshop on Neural Networks},
  year = 	 {2001},
  editor = 	 {Marinaro, Maria and Tagliaferri, Roberto},
  publisher =    {Springer-Verlag},
  pdf = 	 {http://www.thelawrences.net/neil/structure.pdf},
  url = 	 {/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.}
}

Endnote

%0 Conference Paper
%T Node Relevance Determination
%A Neil D. Lawrence
%B The {XI}th Italian Workshop on Neural Networks
%D 2001
%E Maria Marinaro
%E Roberto Tagliaferri	
%F Lawrence:nrd01
%I Springer-Verlag
%U /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.

RIS


TY  - CPAPER
TI  - Node Relevance Determination
AU  - Neil D. Lawrence
BT  - The {XI}th Italian Workshop on Neural Networks
DA  - 2001/01/01
ED  - Maria Marinaro
ED  - Roberto Tagliaferri	
ID  - Lawrence:nrd01
PB  - Springer-Verlag
L1  - http://www.thelawrences.net/neil/structure.pdf
UR  - /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  -

APA


Lawrence, N.D.. (2001). Node Relevance Determination. The {XI}th Italian Workshop on Neural Networks Available from /publications/lawrence-nrd01.html.