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Titlebook: Belief Functions: Theory and Applications; Third International Fabio Cuzzolin Conference proceedings 2014 Springer International Publishin

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發(fā)表于 2025-3-21 16:58:15 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Belief Functions: Theory and Applications
期刊簡稱Third International
影響因子2023Fabio Cuzzolin
視頻videohttp://file.papertrans.cn/184/183303/183303.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Belief Functions: Theory and Applications; Third International  Fabio Cuzzolin Conference proceedings 2014 Springer International Publishin
影響因子This book constitutes the thoroughly refereed proceedings of the Third International Conference on Belief Functions, BELIEF 2014, held in Oxford, UK, in September 2014. The 47 revised full papers presented in this book were carefully selected and reviewed from 56 submissions. The papers are organized in topical sections on belief combination; machine learning; applications; theory; networks; information fusion; data association; and geometry.
Pindex Conference proceedings 2014
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沙發(fā)
發(fā)表于 2025-3-21 23:08:40 | 只看該作者
Industrialization and Challenges in Asiaa new perspective on contextual discounting: it can be seen as successive corrections corresponding to simple contextual lies. Most interestingly, a similar interpretation is provided for the reinforcement process. Two new contextual correction mechanisms, which are similar yet complementary to the
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https://doi.org/10.1007/978-1-349-22442-5vidence. It is optimal in the sense that the resulting combined .-function has the least dissimilarity with the individual .-functions and therefore represents the greatest amount of information similar to that represented by the original .-functions. Examples are provided to illustrate the proposed
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發(fā)表于 2025-3-22 16:21:55 | 只看該作者
https://doi.org/10.1007/978-1-349-22442-5d when dealing with high uncertainty. Many classification approaches such as .-nearest neighbors, neural network or decision trees have been formulated with belief functions. In this paper, we propose an evidential calibration method that transforms the output of a classifier into a belief function.
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發(fā)表于 2025-3-22 20:01:55 | 只看該作者
Michaela Fink,Reimer Gronemeyerered choice data. The contribution of this model is its ability to extract information from all the observed rankings to improve the prediction power for each individual’s primary choice. The evidence-theoretic .-NN rule for heterogeneous rank-ordered data method can be consistently applied to compl
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發(fā)表于 2025-3-23 00:20:44 | 只看該作者
Michaela Fink,Reimer Gronemeyerals with a new approach to cluster uncertain data by using a hierarchical clustering defined within the belief function framework. The main objective of the belief hierarchical clustering is to allow an object to belong to one or several clusters. To each belonging, a degree of belief is associated,
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