Node Relevance Determination

Neil D. Lawrence
, 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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-lawrence-nrd01, title = {Node Relevance Determination}, author = {Neil D. Lawrence}, year = {}, editor = {}, 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.} }
Endnote
%0 Conference Paper %T Node Relevance Determination %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-lawrence-nrd01 %I PMLR %J Proceedings of Machine Learning Research %P -- %U http://inverseprobability.com %V %W PMLR %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 - PY - DA - ED - ID - pmlr-v-lawrence-nrd01 PB - PMLR SP - DP - PMLR EP - L1 - 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 -
APA
Lawrence, N.D.. (). Node Relevance Determination. , in PMLR :-

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