A probability estimation based criterion for model evaluation

Czernichow, Thomas; Muñoz San Roque, Antonio

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Campo DC	Valor	Lengua/Idioma
dc.contributor.author	Czernichow, Thomas	es-ES
dc.contributor.author	Muñoz San Roque, Antonio	es-ES
dc.date.accessioned	2025-03-04T18:47:18Z	-
dc.date.available	2025-03-04T18:47:18Z	-
dc.date.issued	1997-09-29	es_ES
dc.identifier.uri	http://hdl.handle.net/11531/97842	-
dc.description	Capítulos en libros	es_ES
dc.description.abstract		es-ES
dc.description.abstract	We develop a criterion based on the estimation of the joint probability density function (pdf) of the input and the error, and on the pdf of the input. It is made to decide when the couple inputmodel no longer fit together. The estimation of the pdf is made through a Probabilistic Radial Basis Function Network (PRBFN) which can also be used to estimate the given task. We compare the results when using a dedicated network, or when extracting the density value directly from the network which estimates the input-output mapping.	en-GB
dc.format.mimetype	application/pdf	es_ES
dc.language.iso	en-GB	es_ES
dc.publisher	Sin editorial (Lausana, Suiza)	es_ES
dc.rights		es_ES
dc.rights.uri		es_ES
dc.source	Libro: International Conference on Artificial Neural Networks - ICANN '97, Página inicial: 1029-1034, Página final:	es_ES
dc.subject.other	Instituto de Investigación Tecnológica (IIT)	es_ES
dc.title	A probability estimation based criterion for model evaluation	es_ES
dc.type	info:eu-repo/semantics/bookPart	es_ES
dc.description.version	info:eu-repo/semantics/publishedVersion	es_ES
dc.rights.accessRights	info:eu-repo/semantics/restrictedAccess	es_ES
dc.keywords		es-ES
dc.keywords	Neural Networks	en-GB
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