dc.contributor.author | Doherty, K. | |
dc.contributor.author | Adams, R.G. | |
dc.contributor.author | Davey, N. | |
dc.date.accessioned | 2009-10-19T13:11:09Z | |
dc.date.available | 2009-10-19T13:11:09Z | |
dc.date.issued | 2005 | |
dc.identifier.citation | Doherty , K , Adams , R G & Davey , N 2005 , Hierarchical growing neural gas . in 05) . Springer Nature , pp. 140-143 . https://doi.org/10.1007/3-211-27389-1_34 | |
dc.identifier.isbn | 978-3-211-24934-5 | |
dc.identifier.other | dspace: 2299/3968 | |
dc.identifier.uri | http://hdl.handle.net/2299/3968 | |
dc.description | “The original publication is available at www.springerlink.com”. Copyright Springer. | |
dc.description.abstract | This 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.format.extent | 349734 | |
dc.language.iso | eng | |
dc.publisher | Springer Nature | |
dc.relation.ispartof | 05) | |
dc.title | Hierarchical growing neural gas | en |
dc.contributor.institution | School of Computer Science | |
dc.contributor.institution | Science & Technology Research Institute | |
dc.contributor.institution | Centre for Computer Science and Informatics Research | |
rioxxterms.versionofrecord | 10.1007/3-211-27389-1_34 | |
rioxxterms.type | Other | |
herts.preservation.rarelyaccessed | true | |