Yin, Hufun and Allinson, Nigel (1997) Bayesian learning for self-organising maps. Electronics Letters, 33 (4). pp. 304-305. ISSN 0013-5194
Full text not available from this repository.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namely the Bayesian self-organising map (BSOM), in which both the distance measure and neighbourhood function have been replaced by the neuron's `on-line' estimated posterior probabilities.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namely the Bayesian self-organising map (BSOM), in which both the distance measure and neighbourhood function have been replaced by the neuron's `on-line' estimated posterior probabilities.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 Sciences > Faculty of Science > Lincoln School of Computer Science |
| Depositing User: | Bev Jones |
| Date Deposited: | 19 Apr 2012 15:01 |
| Last Modified: | 19 Apr 2012 15:01 |
| URI: | http://eprints.lincoln.ac.uk/id/eprint/5067 |
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