ViSOM Ensembles for Visualization and Classification

Baruque, Bruno; Corchado Rodríguez, Emilio Santiago; Yin, Hujun

Título

dc.contributor.author	Baruque, Bruno
dc.contributor.author	Corchado Rodríguez, Emilio Santiago
dc.contributor.author	Yin, Hujun
dc.date.accessioned	2017-09-06T09:16:05Z
dc.date.available	2017-09-06T09:16:05Z
dc.date.issued	2007
dc.identifier.citation	Computational and Ambient Intelligence. 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, San Sebastián, Spain, June 20-22, 2007. Proceedings. Lecture Notes in Computer Science. Volumen 4507.
dc.identifier.isbn	978-3-540-73006-4 (Print) / 978-3-540-73007-1 (Online)
dc.identifier.issn	0302-9743 (Print) / 1611-3349 (Online)
dc.identifier.uri	http://hdl.handle.net/10366/135050
dc.description.abstract	In this paper ensemble techniques have been applied in the frame of topology preserving mappings in two applications: classification and visualization. These techniques are applied for the first time to the ViSOM and their performance is compared with ensemble combination of some other topology preserving mapping such as the SOM or the MLSIM. Several methods to obtain a meaningful combination of the components of an ensemble are presented and tested together with the existing ones in order to identify the best performing method in the applications of these models.
dc.format.mimetype	application/pdf
dc.language.iso	en
dc.publisher	Springer Science + Business Media
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subject	Computer Science
dc.title	ViSOM Ensembles for Visualization and Classification
dc.type	info:eu-repo/semantics/conferenceObject
dc.rights.accessRights	info:eu-repo/semantics/openAccess