Show simple item record

dc.contributor.authorBéjar Alonso, Javier
dc.contributor.authorCortés García, Claudio Ulises
dc.contributor.authorPoch, Manel
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2016-11-23T09:44:16Z
dc.date.available2016-11-23T09:44:16Z
dc.date.issued1993-04
dc.identifier.citationBejar, J., Cortes, C., Poch, M. "LINNEO+: a classification methodology for ill-structured domains". 1993.
dc.identifier.urihttp://hdl.handle.net/2117/97083
dc.description.abstractIn this work we present a methodology oriented to domains with a weak structure (ill-domains) for, using inductive conceptual learning techniques (descriptive generalization) and classification, discover concepts through observations from the domain, and organize hierarchies with them, in order to, after expert validation, build knowledge bases. Some techniques for the improvement of the results in the classification step are used, like biasing using partial expert knowledge (classification rules or causal and structural dependencies between attributes) or delayed cluster assignation of objects.
dc.language.isoeng
dc.relation.ispartofseriesLSI-93-22-R
dc.subjectÀrees temàtiques de la UPC::Informàtica::Programació
dc.subject.otherIll-domains
dc.subject.otherInductive conceptual learning
dc.subject.otherClassification
dc.subject.otherLINNEO+
dc.titleLINNEO+: a classification methodology for ill-structured domains
dc.typeExternal research report
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.rights.accessOpen Access
drac.iddocument646783
dc.description.versionPostprint (published version)
upcommons.citation.authorBejar, J., Cortes, C., Poch, M.
upcommons.citation.publishedtrue


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder