Davide Aliprandi

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In this paper we propose a Bayesian framework for XCS [9], called BXCS. Following [4], we use probability distributions to represent the uncertainty over the classifier estimates of payoff. A novel interpretation of classifier and an extension of the accuracy concept are presented. The probabilistic approach is aimed at increasing XCS learning capabilities(More)
In this paper a content-independent, adaptive and parameter-free estimation chain for depth map generation from still-images stereo content will be described. The state-of-the-art solutions for each step of the classical chain will be analyzed as reference benchmark for our proposed implementation. Finally, comparative results will be presented and(More)
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