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dc.contributor.authorButchart, K.
dc.date.accessioned2010-10-07T11:19:42Z
dc.date.available2010-10-07T11:19:42Z
dc.date.issued1994
dc.identifier.citationButchart , K 1994 , A comparative study of three neural networks that use soft competition . UH Computer Science Technical Report , vol. 211 , University of Hertfordshire .
dc.identifier.otherPURE: 100085
dc.identifier.otherPURE UUID: c204cf20-1b29-483c-823a-3df5fc804ba7
dc.identifier.otherdspace: 2299/4895
dc.identifier.urihttp://hdl.handle.net/2299/4895
dc.description.abstractThis report provides a comparative study of three proposed self-organising neural network models that use forms of soft competition. The use of soft competition helps the neural networks to avoid poor local minima and so provide a better interpretation of the data they are representing. The networks are also thought to be generally insensitive to initialisation conditions. The networks studied are the Deterministic Soft Competition Network (DSCN) of Yair et al., the Neural Gas network of Martinetz et al and the Generalised Learning Vector Quantisation (GLVQ) of Pal et al. The performance of the networks is compared to that of standard competitive networks and a Self Organising Map when run over a variety of data sets. The three proposed neural network models appear to produce enhanced results, particularly the Neural Gas network, but in case of the Neural Gas network and the DSCN this is at the cost of greater computational complexity.en
dc.language.isoeng
dc.publisherUniversity of Hertfordshire
dc.relation.ispartofseriesUH Computer Science Technical Report
dc.titleA comparative study of three neural networks that use soft competitionen
dc.contributor.institutionSchool of Computer Science
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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