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

LEVER

HUMID

An Evidential Improvement for Gender Profiling,ld 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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An Interval-Valued Dissimilarity Measure for Belief Functions Based on Credal Semantics, 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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过于平凡

A Comparison between a Bayesian Approach and a Method Based on Continuous Belief Functions for Patt 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

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Prognostic by Classification of Predictions Combining Similarity-Based Estimation and Belief Functind Health Management (PHM). Practically, states can be either continuous (the value of a signal) or discrete (functioning modes). For each case, specific techniques exist. In this paper, we propose an approach called EVIPRO-KNN based on case-based reasoning and belief functions that jointly estimate

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Adaptive Initialization of a EvKNN Classification Algorithm,ial KNN (EvKNN) has been developed in order to help the user, which proposes the “best” samples to label according to a strategy. However, at the beginning of this task, the classes are not clearly defined and are represented by a number of labeled samples smaller than the k required samples for EvK

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Classification Trees Based on Belief Functions,functions previously described for two-class problems only. We propose three possible extensions: combining multiple two-class trees together and directly extending the estimation of belief functions within the tree to the multi-class setting. We provide experiment results and compare them to usual

PLUMP

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Continuous Belief Functions: Focal Intervals Properties,us very difficult to represent..In this paper, we propose a graphical representation of the cross product of two focal sets originating from univariate Gaussian pdfs. This representation allows to represent initial focal intervals as well as focal intervals resulting from a combination operation. We