A Learning Classifier Systems Bibliography
نویسندگان
چکیده
[6] Jose Aguilar and Mariela Cerrada. Fuzzy classifier system and genetic programming on system identification problems. In Lee Spector, Erik D. Goodman, Annie Wu, W.B. Langdon, Hans-Michael Voigt, Mitsuo Gen, Sandip Sen, Marco Dorigo, Shahram Pezeshk, Max H. Garzon, and Edmund Burke, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 1245–1251, San Francisco, California, USA, 7-11 July 2001. Morgan Kaufmann.
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NEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS
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متن کاملThe Introduction of a Heuristic Mutation Operator to Strengthen the Discovery Component of XCS
The extended classifier systems (XCS) by producing a set of rules is (classifier) trying to solve learning problems as online. XCS is a rather complex combination of genetic algorithm and reinforcement learning that using genetic algorithm tries to discover the encouraging rules and value them by reinforcement learning. Among the important factors in the performance of XCS is the possibility to...
متن کاملThe Introduction of a Heuristic Mutation Operator to Strengthen the Discovery Component of XCS
The extended classifier systems (XCS) by producing a set of rules is (classifier) trying to solve learning problems as online. XCS is a rather complex combination of genetic algorithm and reinforcement learning that using genetic algorithm tries to discover the encouraging rules and value them by reinforcement learning. Among the important factors in the performance of XCS is the possibility to...
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This study explores a semi-supervised classification approach using random forest as a base classifier to classify the low-back disorders (LBDs) risk associated with the industrial jobs. Semi-supervised classification approach uses unlabeled data together with the small number of labelled data to create a better classifier. The results obtained by the proposed approach are compared with those o...
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