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- W2412957144 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." @default.
- W2412957144 created "2016-06-24" @default.
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- W2412957144 date "1995-04-01" @default.
- W2412957144 modified "2023-09-27" @default.
- W2412957144 title "Probabilistic and fuzzy reasoning in simple learning classifier systems" @default.
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