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dc.contributor.authorLeón, Rafael
dc.contributor.authorRainer, J. Javier
dc.contributor.authorRojo, José Manuel
dc.contributor.authorGalán, Ramón
dc.date2012-09
dc.date.accessioned2019-12-03T09:57:24Z
dc.date.available2019-12-03T09:57:24Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/9603
dc.description.abstractWe perform a review of Web Mining techniques and we describe a Bootstrap Statistics methodology applied to pattern model classifier optimization and verification for Supervised Learning for Tour-Guide Robot knowledge repository management. It is virtually impossible to test thoroughly Web Page Classifiers and many other Internet Applications with pure empirical data, due to the need for human intervention to generate training sets and test sets. We propose using the computer-based Bootstrap paradigm to design a test environment where they are checked with better reliability.es_ES
dc.language.isospaes_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.ispartofseries;vol. 01, nº 06
dc.relation.urihttps://www.ijimai.org/journal/node/273es_ES
dc.rightsopenAccesses_ES
dc.subjectweb mininges_ES
dc.subjectsupervised learninges_ES
dc.subjectbootstrapes_ES
dc.subjectpatterns mininges_ES
dc.subjectweb classifierses_ES
dc.subjectknowledge managementes_ES
dc.subjectIJIMAIes_ES
dc.titleImproving Web Learning through model Optimization using Bootstrap for a Tour-Guide Robotes_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttp://dx.doi.org/10.9781/ijimai.2012.162


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