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dc.contributor.authorPensuwon, W.
dc.contributor.authorAdams, R.G.
dc.contributor.authorDavey, N.
dc.date.accessioned2009-10-20T08:16:51Z
dc.date.available2009-10-20T08:16:51Z
dc.date.issued2004
dc.identifier.citationPensuwon , W , Adams , R G & Davey , N 2004 , ' Optimising a Neural Tree Using Subtree Retraining ' , Lecture Notes in Computer Science , vol. 2004 , pp. 256-262 .
dc.identifier.issn0302-9743
dc.identifier.otherPURE: 94964
dc.identifier.otherPURE UUID: 0a81deb9-9ea9-4ca8-87ae-34c2e39b6fe2
dc.identifier.otherdspace: 2299/3972
dc.identifier.otherScopus: 84975521862
dc.identifier.urihttp://hdl.handle.net/2299/3972
dc.description“The original publication is available at www.springerlink.com”. Copyright Springer. [Full text of this article is not available in the UHRA]
dc.description.abstractSubtree retraining applied to a Stochastic Competitive Evolutionary Neural Tree model (SCENT) is introduced. This subtree retraining process is designed to improve the performance of the original model which provides a hierarchical classification of unlabelled data. The effect of subtree retraining on the network produces stable classificatory structures by repeatedly restructuring the weakest branch of the classification tree based on internal relation between members. An experimental comparison using well-known real world data sets, chosen to provide a variety of clustering scenarios, showed the new approach produced more reliable performances.en
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science
dc.titleOptimising a Neural Tree Using Subtree Retrainingen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionScience & Technology Research Institute
dc.description.statusPeer reviewed
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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