Bayesian learning for self-organising maps

Yin, Hufun and Allinson, Nigel (1997) Bayesian learning for self-organising maps. Electronics Letters, 33 (4). pp. 304-305. ISSN 0013-5194

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Full text URL: http://dx.doi.org/10.1049/el:19970196

Abstract

An extended self-organising learning scheme is proposed, namely the Bayesian self-organising map (BSOM), in which both the distance measure and neighbourhood function have been replaced by the neuron's `on-line' estimated posterior probabilities. Such posteriors, in a Bayesian inference sense, will then contribute to gradually sharpening the estimation for input distributions and model parameters for which generally there is little prior knowledge. The BSOM has been successfully used to team the underlying mixture distribution of input data, and hence form an optimal pattern classifier

Item Type:Article
Additional Information:An extended self-organising learning scheme is proposed, namely the Bayesian self-organising map (BSOM), in which both the distance measure and neighbourhood function have been replaced by the neuron's `on-line' estimated posterior probabilities. Such posteriors, in a Bayesian inference sense, will then contribute to gradually sharpening the estimation for input distributions and model parameters for which generally there is little prior knowledge. The BSOM has been successfully used to team the underlying mixture distribution of input data, and hence form an optimal pattern classifier
Keywords:Self-Organising Map, Data Clustering, Vector quantisation, Pattern Classification
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:5067
Deposited By: Bev Jones
Deposited On:19 Apr 2012 15:01
Last Modified:19 Apr 2012 15:01

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