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dc.contributor.authorBaruque, Bruno
dc.contributor.authorCorchado Rodríguez, Emilio Santiago 
dc.contributor.authorYin, Hujun
dc.date.accessioned2017-09-06T09:16:00Z
dc.date.available2017-09-06T09:16:00Z
dc.date.issued2007
dc.identifier.citationIntelligent Data Engineering and Automated Learning - IDEAL 2007. 8th International Conference, Birmingham, UK, December 16-19, 2007. Proceedings. Lecture Notes in Computer Science. Volumen 4881.
dc.identifier.isbn978-3-540-77225-5 (Print) / 978-3-540-77226-2 (Online)
dc.identifier.issn0302-9743 (Print) / 1611-3349 (Online)
dc.identifier.urihttp://hdl.handle.net/10366/135042
dc.description.abstractThis work presents a research on the performance capabilities of an extension of the ViSOM (Visualization Induced SOM) algorithm by the use of the ensemble meta-algorithm and a later fusion process. This main fusion process has two different variants, considering two different criteria for the similarity of nodes. These criteria are Euclidean distance and similarity on Voronoi polygons. The capabilities, strengths and weakness of the different variants of the model are discussed and compared more deeply in the present work. The details of several experiments performed over different datasets applying the variants of the fusion to the ViSOM algorithm along with same variants of fusion with the SOM are included for this purpose.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringer Science + Business Media
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titleQuality of Adaptation of Fusion ViSOM
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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