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dc.contributor.authorCorchado Rodríguez, Juan Manuel 
dc.contributor.authorCorchado Rodríguez, Emilio Santiago 
dc.contributor.authorAiken, Jim
dc.contributor.authorFyfe, Colin
dc.contributor.authorFernández Riverola, Florentino
dc.contributor.authorM. Glez-Bedia
dc.date.accessioned2017-09-06T09:16:37Z
dc.date.available2017-09-06T09:16:37Z
dc.date.issued2003/06
dc.identifier.citationLecture Notes in Computer Science Current Topics in Artificial Intelligence. 5th International Conference on Case-Based Reasoning, ICCBR 2003 Trondheim, Norway, June 23–26, 2003 Proceedings. Lecture Notes in Computer Science. Volumen 2689, pp. 107-121.
dc.identifier.isbn978-3-540-40433-0 (Print) / 978-3-540-45006-1 (Online)
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/10366/135109
dc.description.abstractCBR systems are normally used to assist experts in the resolution of problems. During the last few years researchers have been working in the development of techniques to automate the reasoning stages identified in this methodology. This paper presents a Maximum Likelihood Hebbian Learning-based method that automates the organisation of cases and the retrieval stage of casebased reasoning systems. The proposed methodology has been derived as an extension of the Principal Component Analysis, and groups similar cases, identifying clusters automatically in a data set in an unsupervised mode. The method has been successfully used to completely automate the reasoning process of an oceanographic forecasting system and to improve its performance.
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.titleMaximum Likelihood Hebbian Learning Based Retrieval Method for CBR Systems
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