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Probabilistic and fuzzy reasoning in simple learning classifier systems

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dc.affiliation.dptoUC3M. Departamento de EstadĂ­sticaes
dc.contributor.authorMuruzábal, Jorge
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de EstadĂ­stica
dc.date.accessioned2009-05-13T07:38:49Z
dc.date.available2009-05-13T07:38:49Z
dc.date.issued1995-04
dc.description.abstractThis paper is concerned with the general stimulus-response problem as addressed by a variety of simple learning c1assifier systems (CSs). We suggest a theoretical model from which the assessment of uncertainty emerges as primary concern. A number of representation schemes borrowing from fuzzy logic theory are reviewed, and sorne connections with a well-known neural architecture revisited. In pursuit of the uncertainty measuring goal, usage of explicit probability distributions in the action part of c1assifiers is advocated. Sorne ideas supporting the design of a hybrid system incorpo'rating bayesian learning on top of the CS basic algorithm are sketched.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10016/4201
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries1995-14-03
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEstadĂ­stica
dc.subject.otherPrediction
dc.subject.otherBayesian learning
dc.subject.otherFuzzy logic
dc.subject.otherUncertainty measuring
dc.titleProbabilistic and fuzzy reasoning in simple learning classifier systems
dc.typeworking paper*
dspace.entity.typePublication
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