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dc.contributor.authorPiedra Fernández, José Antonio
dc.contributor.authorSalmerón Cerdán, Antonio
dc.contributor.authorGuindos, Francisco J.
dc.contributor.authorCantón-Garbín, Manuel
dc.date.accessioned2012-05-28T08:15:58Z
dc.date.available2012-05-28T08:15:58Z
dc.date.issued2005
dc.identifier.citationActas de la VI Jornadas de Transferencia de Tecnología en I.A., pp. 133-140.es_ES
dc.identifier.urihttp://hdl.handle.net/10835/1545
dc.description.abstractThis paper describes the use of Bayesian networks for the reduction of irrelevant features [1,2] in the recognition of oceanic structures in satellite images. Bayesian networks are used to validate the symbolic knowledge -provided by neuro symbolic or HLKPs (High Level Knowledge Processors) nets- and the numeric knowledge. This provides an automatic interpretation of images. The main objective of this work is the construction of an automatic recognition system for processing AVHRR (Advanced Very High Resolution Radiometer) images from NOAA (National Oceanographic and Atmospheric Administration) satellites to detect and locate oceanic phenomena of interest like upwellings, eddies and island wakes. With this aim, this paper reports on a methodology of knowledge selection and validation. In knowledge selection, filter measures are used. For knowledge validation, Bayesian networks (Naïve Bayes, TAN and KDB) are evaluated.es_ES
dc.language.isoenes_ES
dc.sourceVI Jornadas de Transferencia de Tecnología en I.A.es_ES
dc.titleReduction of Irrelevant Features in Oceanic Satellite Images by means of Bayesian Networkses_ES
dc.typeinfo:eu-repo/semantics/reportes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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