Gray Codes for Partial Match and Range Queries
IEEE Transactions on Software Engineering
Classifier systems and genetic algorithms
Artificial Intelligence
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Incremental quantile estimation for massive tracking
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Reinforcement Learning
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Classifiers that approximate functions
Natural Computing: an international journal
Accuracy-based Neuro And Neuro-fuzzy Classifier Systems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
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Previous work [1] introduced a new approach to value function approximation in classifier systems called hyperplane coding. Hyperplane coding is a closely related variation of tile coding [13] in which classifier rule conditions fill the role of tiles, and there are few restrictions on the way those "tiles" are organized. Experiments with hyperplane coding have shown that, given a relatively small population of random classifiers, it computes much better approximations than more conventional classifier system methods in which individual rules compute approximations independently. The obvious next step in this line of research is to use the approximation resources available in a random population as a starting point for a more refined approach to approximation that re-allocates resources adaptively to gain greater precision in those regions of the input space where it is needed. This paper shows how to compute such an adaptive function approximation.