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dc.contributor.authorButchart, K.
dc.date.accessioned2010-11-15T14:52:05Z
dc.date.available2010-11-15T14:52:05Z
dc.date.issued1995
dc.identifier.citationButchart , K 1995 , Investigation of self-organising dynamic neural tree networks . UH Computer Science Technical Report , vol. 225 , University of Hertfordshire .
dc.identifier.otherPURE: 99773
dc.identifier.otherPURE UUID: 18a2c64d-9d61-43da-bb88-774f0615f8eb
dc.identifier.otherdspace: 2299/4990
dc.identifier.urihttp://hdl.handle.net/2299/4990
dc.description.abstractSelf Organising Dynamic Neural Tree Networks (DNTNs) provide hierarchical clustering that is potentially applicable to large data sets. Two DNTN models have been produced by Racz and Klotz and Li et al. Four DNTN variants have been developed and analysed in order to establish which of the four potential cluster expansion methods is the most robust to parameter alterations in the production of representative tree structures.en
dc.language.isoeng
dc.publisherUniversity of Hertfordshire
dc.relation.ispartofseriesUH Computer Science Technical Report
dc.titleInvestigation of self-organising dynamic neural tree networksen
dc.contributor.institutionSchool of Computer Science
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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