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dc.contributor.authorHüllermeier, Eyke
dc.contributor.authorRenners, Ingo
dc.contributor.authorGrauel, Adolf
dc.date.accessioned2007-10-05T09:07:12Z
dc.date.available2007-10-05T09:07:12Z
dc.date.issued2004
dc.identifier.issn1134-5632
dc.identifier.urihttp://hdl.handle.net/2099/3641
dc.description.abstractThe success of machine learning methods for inducing models from data crucially depends on the proper incorporation of background knowledge about the model to be learned. The idea of constraint-regularized learning is to em- ploy fuzzy set-based modeling techniques in order to express such knowl- edge in a flexible way, and to formalize it in terms of fuzzy constraints. Thus, background knowledge can be used to appropriately bias the learn- ing process within the regularization framework of inductive inference. After a brief review of this idea, the paper offers an operationalization of constraint- regularized learning. The corresponding framework is based on evolutionary methods for model optimization and employs fuzzy rule bases of the Takagi- Sugeno type as flexible function approximators.
dc.format.extent109-124
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.relation.ispartofMathware & soft computing . 2004 Vol. 11 Núm. 3
dc.rightsReconeixement-NoComercial-CompartirIgual 3.0 Espanya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.otherCLR
dc.subject.otherLocal structural constraints
dc.subject.otherOptimization search methods
dc.titleAn evolutionary approach to constraint-regularized learning
dc.typeArticle
dc.subject.lemacIntel·ligència artificial
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacReconeixement de formes (Informàtica)
dc.subject.amsClassificació AMS::68 Computer science::68T Artificial intelligence
dc.rights.accessOpen Access


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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain