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An evolutionary approach to constraint-regularized learning
dc.contributor.author | Hüllermeier, Eyke |
dc.contributor.author | Renners, Ingo |
dc.contributor.author | Grauel, Adolf |
dc.date.accessioned | 2007-10-05T09:07:12Z |
dc.date.available | 2007-10-05T09:07:12Z |
dc.date.issued | 2004 |
dc.identifier.issn | 1134-5632 |
dc.identifier.uri | http://hdl.handle.net/2099/3641 |
dc.description.abstract | The 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.extent | 109-124 |
dc.language.iso | eng |
dc.publisher | Universitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica |
dc.relation.ispartof | Mathware & soft computing . 2004 Vol. 11 Núm. 3 |
dc.rights | Reconeixement-NoComercial-CompartirIgual 3.0 Espanya |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject.other | CLR |
dc.subject.other | Local structural constraints |
dc.subject.other | Optimization search methods |
dc.title | An evolutionary approach to constraint-regularized learning |
dc.type | Article |
dc.subject.lemac | Intel·ligència artificial |
dc.subject.lemac | Aprenentatge automàtic |
dc.subject.lemac | Reconeixement de formes (Informàtica) |
dc.subject.ams | Classificació AMS::68 Computer science::68T Artificial intelligence |
dc.rights.access | Open Access |
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2004, Vol. XI, Núm. 2-3 [12]
"Fuzzy systems: from modelling to knowledge extraction"