Probabilistic and fuzzy reasoning in simple learning classifier systems

Repositorio e-Archivo

Mostrar el registro sencillo del ítem Muruzábal, Jorge
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística 2009-05-13T07:38:49Z 2009-05-13T07:38:49Z 1995-04
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.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
 Find Full text

Ficheros en el ítem

*Click en la imagen del fichero para previsualizar.(Los elementos embargados carecen de esta funcionalidad)

El ítem tiene asociada la siguiente licencia:

Este ítem aparece en la(s) siguiente(s) colección(es)

Mostrar el registro sencillo del ítem