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dc.contributor.authorKoetsier, Jos
dc.contributor.authorCorchado Rodríguez, Emilio Santiago 
dc.contributor.authorMacDonald, Donald
dc.contributor.authorCorchado Rodríguez, Juan Manuel 
dc.contributor.authorFyfe, Colin
dc.date.accessioned2017-09-06T09:16:28Z
dc.date.available2017-09-06T09:16:28Z
dc.date.issued2004
dc.identifier.citationLecture Notes in Computer Science Computational Science - ICCS 2004. Lecture Notes in Computer Science. Volumen 3037, pp. 650-653.
dc.identifier.isbn978-3-540-22115-9 (Print) / 978-3-540-24687-9 (Online)
dc.identifier.issn0302-9743 (Print) / 1611-3349 (Online)
dc.identifier.urihttp://hdl.handle.net/10366/135093
dc.description.abstractWe present a novel method based on a recently proposed extension to a negative feedback network which uses simple Hebbian learning to self-organise called Maximum Likelihood Hebbian learning [2]. We use the kernel version of the ML algorithm on data from a spectroscopic analysis of a stained glass rose window in a Spanish cathedral. It is hoped that in classifying the origin and date of each segment it will help in the restoration of this and other historical stain glass windows.
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.titleKernel Maximum Likelihood Hebbian Learning
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