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dc.contributor.authorBarson, P.
dc.contributor.authorDavey, N.
dc.contributor.authorFrank, R.
dc.contributor.authorTansley, D.
dc.date.accessioned2010-11-04T15:51:47Z
dc.date.available2010-11-04T15:51:47Z
dc.date.issued1995
dc.identifier.citationBarson , P , Davey , N , Frank , R & Tansley , D 1995 , Dynamic competitive learning applied to the clone detection problem . UH Computer Science Technical Report , vol. 220 , University of Hertfordshire .
dc.identifier.otherPURE: 87958
dc.identifier.otherPURE UUID: fcf92478-a78f-435e-8154-5d7ef0894fb4
dc.identifier.otherdspace: 2299/4963
dc.identifier.urihttp://hdl.handle.net/2299/4963
dc.description.abstractA novel neural network architecture, 'Dynamic Competitive Learning', has been applied to the software clone detection problem in telecommunications systems. A software clone is a copy (subsequently modified), of a piece of existing code; it is common practice in large systems to obtain similar code via reuse of fully tested code. This paper discusses a hierarchical network with dramatically reduced training time compared to a Self Organising Map, but with at least as good a classification performance.en
dc.language.isoeng
dc.publisherUniversity of Hertfordshire
dc.relation.ispartofseriesUH Computer Science Technical Report
dc.titleDynamic competitive learning applied to the clone detection problemen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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