Davide Aliprandi

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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)
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)
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