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dc.contributor.authorDoherty, K.
dc.contributor.authorAdams, R.G.
dc.contributor.authorDavey, N.
dc.date.accessioned2009-10-19T13:11:09Z
dc.date.available2009-10-19T13:11:09Z
dc.date.issued2005
dc.identifier.citationDoherty , K , Adams , R G & Davey , N 2005 , Hierarchical growing neural gas . in 05) . Springer , pp. 140-143 . https://doi.org/10.1007/3-211-27389-1_34
dc.identifier.isbn978-3-211-24934-5
dc.identifier.otherPURE: 98517
dc.identifier.otherPURE UUID: 35e5f7d6-b62a-41f5-bbff-9abd1c3d1859
dc.identifier.otherdspace: 2299/3968
dc.identifier.urihttp://hdl.handle.net/2299/3968
dc.description“The original publication is available at www.springerlink.com”. Copyright Springer.
dc.description.abstractThis paper describes TreeGNG, a top-down unsupervised learning method that produces hierarchical classification schemes. TreeGNG is an extension to the Growing Neural Gas algorithm that maintains a time history of the learned topological mapping. TreeGNG is able to correct poor decisions made during the early phases of the construction of the tree, and provides the novel ability to influence the general shape and form of the learned hierarchy.en
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartof05)
dc.rightsOpen
dc.titleHierarchical growing neural gasen
dc.contributor.institutionSchool of Computer Science
dc.relation.schoolSchool of Computer Science
dcterms.dateAccepted2005
rioxxterms.versionofrecordhttps://doi.org/10.1007/3-211-27389-1_34
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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