Titlebook: Belief Functions: Theory and Applications; Proceedings of the 2 Thierry Denoeux,Marie-Hélène Masson Conference proceedings 2012 Springer-Ve

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https://doi.org/10.1007/978-3-642-29461-7Belief Functions; Intelligent Computing

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978-3-642-29460-0Springer-Verlag Berlin Heidelberg 2012

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Jeffrey Henderson,Richard WhitleyA.EL, randomly breaks the initial set of labels into smaller sets and trains a single-label classifier in each of this subset. To classify an unseen instance, the predictions of all classifiers are combined using a voting process. In this paper, we adapt the RA.EL approach under the belief function

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https://doi.org/10.1007/978-1-349-26520-6ld applications to determine the gender of people of interest. However, normal video algorithms for gender profiling (usually face profiling) have three drawbacks. First, the profiling result is always uncertain. Second, for a time-lasting gender profiling algorithm, the result is not stable. The de

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International Political Economy Series of belief functions, where uncertainty corresponds to probability masses which might refer to whole subsets of the possibility space, . semantics can be also considered. Accordingly, a belief function can be identified with the whole set of probability mass functions consistent with the beliefs ind

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A. H. Zijderveld,P. G. Stoutjesdijkef decision space rather than directly in the feature space as traditionally done. The main goal of the paper is to compare performances obtained when using several distances between belief functions recently introduced by the authors. Belief functions are modeled using the outputs of a set of modal

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The Role of Starch in Bread Staling, provided that once the probability density functions are well estimated. Recently, the theory of belief functions has been more and more developed to the continuous case. In this paper, we compare results obtained by a Bayesian approach and a method based on continuous belief functions to character