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Probabilistic and fuzzy reasoning in simple learning classifier systems

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  • Muruzábal, Jorge

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.

Suggested Citation

  • Muruzábal, Jorge, 1995. "Probabilistic and fuzzy reasoning in simple learning classifier systems," DES - Working Papers. Statistics and Econometrics. WS 4201, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:4201
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    Keywords

    Prediction;

    Statistics

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