Yin, Hufun and Allinson, Nigel (1995) Towards the optimal Bayes classifier using an extended self-organising map. In: International Conference on Artificial Neural Networks 1995, 9-13 October 1995, Paris, France.

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Abstract

In this paper, we propose an extended self-organising learning scheme, in which both distance measure and neighbourhood function have been replaced by the neuron's posterior probabilities. Updating of weights is within a limited but fixed sized neighbourhood of the winner. Each unit will converge to one component of a mixture distribution of input samples, so that an optimal pattern classifier can be formed. The proposed learning scheme can be used to train other forms of unsupervised networks, such as radial-basis-function networks. An application example on textured image segmentation is presented.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	In this paper, we propose an extended self-organising learning scheme, in which both distance measure and neighbourhood function have been replaced by the neuron's posterior probabilities. Updating of weights is within a limited but fixed sized neighbourhood of the winner. Each unit will converge to one component of a mixture distribution of input samples, so that an optimal pattern classifier can be formed. The proposed learning scheme can be used to train other forms of unsupervised networks, such as radial-basis-function networks. An application example on textured image segmentation is presented.
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:	Tammie Farley
Date Deposited:	19 Apr 2012 14:56
Last Modified:	13 Mar 2013 09:05
URI:	http://eprints.lincoln.ac.uk/id/eprint/4996

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