Yin, H. and Allinson, N. M. (1999) Interpolating self-organising map (iSOM). Electronics Letters, 35 (19). pp. 1649-1650. ISSN 0013-5194

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Abstract

A new learning algorithm is presented for enhancing the scale or structure of an already trained self-organising map (SOM) without the need to re-use the original training data. Alternative methods for the insertion of these additional interpolating neurons, while still preserving the learnt topology, are presented together with two illustrative examples of the algorithm in operation

Item Type:	Article
Additional Information:	A new learning algorithm is presented for enhancing the scale or structure of an already trained self-organising map (SOM) without the need to re-use the original training data. Alternative methods for the insertion of these additional interpolating neurons, while still preserving the learnt topology, are presented together with two illustrative examples of the algorithm in operation
Keywords:	data visualisation, interpolation, artificial intelligence, self-organising feature maps, algorithm
Subjects:	G Mathematical and Computer Sciences > G500 Information Systems G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User:	Tammie Farley
Date Deposited:	01 May 2012 13:59
Last Modified:	13 Mar 2013 09:06
URI:	http://eprints.lincoln.ac.uk/id/eprint/5129

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