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dc.contributor.authorMacDonald, Donald
dc.contributor.authorKoetsier, Jos
dc.contributor.authorCorchado Rodríguez, Emilio Santiago 
dc.contributor.authorFyfe, Colin
dc.contributor.authorCorchado Rodríguez, Juan Manuel 
dc.date.accessioned2017-09-06T09:16:29Z
dc.date.available2017-09-06T09:16:29Z
dc.date.issued2004/04
dc.identifier.citationMICAI 2004: Advances in Artificial Intelligence Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 2972, pp. 823-832.
dc.identifier.isbn978-3-540-21459-5 (Print) / 978-3-540-24694-7 (Online)
dc.identifier.issn0302-9743 (Print) / 1611-3349 (Online)
dc.identifier.urihttp://hdl.handle.net/10366/135094
dc.description.abstractKernel Maximum Likelihood Hebbian Learning Scale Invariant Maps is a novel technique developed to facilitate the clustering of complex data effectively and efficiently and that is characterised for converging remarkably quickly. The combination of Maximum Likelihood Hebbian Learning Scale Invariant Map and the Kernel Space provides a very smooth scale invariant quantisation which can be used as a clustering technique. The efficiency of this method have been used to analyse an oceanographic problem.
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.titleA Kernel Method for Classification
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported