Adaptive filter theory
Linear least-squares algorithms for temporal difference learning
Machine Learning - Special issue on reinforcement learning
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Classifiers that approximate functions
Natural Computing: an international journal
Least-Squares Temporal Difference Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
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XCSF is an extension of XCS in which classifier prediction is computed as a linear combination of classifier inputs and a weight vector associated to each classifier. XCSF can adjust the weight vector of classifiers to evolve accurate piecewise linear approximations of functions. The Widrow-Hoff rule, used to update the weight vectors, prevents (when some conditions hold) XCSF from exploiting the expected piece-wise linear approximation. In this paper we replace the Widrow-Hoff rule with linear least-squares and we show that with this improvement XCSF can fully exploit its generalization capabilities.