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Estimating Classifier Generalization and Action's Effect: A Minimalist Approach
Pier Luca Lanzi
Artificial Intelligence and Robotics Laboratory
Dipartimento di Elettronica e Informazione
Politecnico di Milano
pierluca.lanzi@polimi.it
Abstract. We present an online technique to estimate the generality of classifiers conditions. We show that this technique can be extended to gather some basic information about the effect of classifier actions in the environment. The approach we present is minimalist in that it is aimed at obtaining as much information as possible from online experience, with as few modifications as possible to the classifier structure. Because of its plainness, the method we propose can be applied virtually to any classifier system model.
LNCS 2724, p. 1894 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003