Probabilistic and fuzzy reasoning in simple learning classifier systems

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dc.contributor.author Muruzábal, Jorge
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2009-05-13T07:38:49Z
dc.date.available 2009-05-13T07:38:49Z
dc.date.issued 1995-04
dc.identifier.uri http://hdl.handle.net/10016/4201
dc.description.abstract This 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.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries 1995-14-03
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Prediction
dc.subject.other Bayesian learning
dc.subject.other Fuzzy logic
dc.subject.other Uncertainty measuring
dc.title Probabilistic and fuzzy reasoning in simple learning classifier systems
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
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