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

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發(fā)表于 2025-3-21 17:05:46 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Belief Functions: Theory and Applications
期刊簡(jiǎn)稱Proceedings of the 2
影響因子2023Thierry Denoeux,Marie-Hélène Masson
視頻videohttp://file.papertrans.cn/184/183299/183299.mp4
發(fā)行地址Latest research on theory and applications Belief functions.Results of the 2nd International Conference on Belief Functions, Compiègne, France 9-11 May 2012.Written by leading experts in the field
學(xué)科分類Advances in Intelligent and Soft Computing
圖書(shū)封面Titlebook: Belief Functions: Theory and Applications; Proceedings of the 2 Thierry Denoeux,Marie-Hélène Masson Conference proceedings 2012 Springer-Ve
影響因子.The theory of belief functions, also known as evidence theory or Dempster-Shafer theory, was first introduced by Arthur P. Dempster in the context of statistical inference, and was later developed by Glenn Shafer as a general framework for modeling epistemic uncertainty. These early contributions have been the starting points of many important developments, including the Transferable Belief Model and the Theory of Hints. The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well understood connections to other frameworks such as probability, possibility and imprecise probability theories..?.This volume contains the proceedings of the 2.nd. International Conference on Belief Functions that was held in Compiègne, France on 9-11 May 2012. It gathers 51 contributions describing recent developments both on theoretical issues (including approximation methods, combination rules, continuous belief functions, graphical models and independence concepts) and applications in various areas including classification, image processing, statistics and intelligent vehicles..?.
Pindex Conference proceedings 2012
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An Interval-Valued Dissimilarity Measure for Belief Functions Based on Credal Semantics,uced by the masses. Following this interpretation, a novel, set-valued, dissimilarity measure with a clear behavioral interpretation can be defined. We describe the main features of this new measure and comment the relation with other measures proposed in the literature.
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Prognostic by Classification of Predictions Combining Similarity-Based Estimation and Belief Functis the future values of the continuous signal and of the future discrete modes. A real datasets is used in order to assess the performance in estimating future break-down of a real system where the combination of both strategies provide the best prediction accuracies, up to 90%.
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Adaptive Initialization of a EvKNN Classification Algorithm,NN. In this paper, we propose to take into account the available information on the classes using an adapted evidential model. The algorithm presented in this paper has been tested on the classification of an image collection.
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https://doi.org/10.1007/978-94-009-4480-0s the future values of the continuous signal and of the future discrete modes. A real datasets is used in order to assess the performance in estimating future break-down of a real system where the combination of both strategies provide the best prediction accuracies, up to 90%.
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