RT Generic
T1 Probabilistic and fuzzy reasoning in simple learning classifier systems
A1 Muruzábal, Jorge
A2 Universidad Carlos III de Madrid. Departamento de Estadística, 
AB 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.
YR 1995
FD 1995-04
LK http://hdl.handle.net/10016/4201
UL http://hdl.handle.net/10016/4201
LA eng
DS e-Archivo
RD 28 abr. 2024