Yin, H. and Allinson, N. M. (1997) Bayesian learning for self-organising maps. Electronic Letters, 33 (4). pp. 304-305. ISSN UNSPECIFIED

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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namely the Bayesian self-organising map (BSOM), in which both the distance measure and neighbourhood function have been replaced by the neuron's `on-line' estimated posterior probabilities.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namely the Bayesian self-organising map (BSOM), in which both the distance measure and neighbourhood function have been replaced by the neuron's `on-line' estimated posterior probabilities.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:	Bayes methods, artificial intelligence, pattern classification, probability, self-organising feature maps
Subjects:	G Mathematical and Computer Sciences > G700 Artificial Intelligence
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User:	Tammie Farley
Date Deposited:	20 Apr 2012 17:32
Last Modified:	13 Mar 2013 09:06
URI:	http://eprints.lincoln.ac.uk/id/eprint/5080

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