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A Generalized Belief Entropy With Nonspecificity and Structural Conflict
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022The Dempster-Shafer (D-S) evidence theory has been widely used in many fields from probabilistic inference, information fusion to decision analysis due to its superiority to formulate uncertain and incomplete information under a weaker condition than the Bayesian probability theory.
Mi Zhou +4 more
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Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy
Information Fusion, 2019Abstract Multi-sensor data fusion technology plays an important role in real applications. Because of the flexibility and effectiveness in modeling and processing the uncertain information regardless of prior probabilities, Dempster–Shafer evidence theory is widely applied in a variety of fields of information fusion.
Fuyuan Xiao
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Conditional plausibility entropy of belief functions based on Dempster conditioning
Information ScienceszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xinyang Deng, Wen Jiang, Xiaoge Zhang
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Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis
Chaos, Solitons & Fractals, 2022Huizi Cui +3 more
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Sparse maximum entropy deep belief nets
The 2013 International Joint Conference on Neural Networks (IJCNN), 2013In this paper, we present a sparse maximum entropy (SME) learning algorithm for deep belief net (DBN). The SME algorithm aims to maximize the entropy and encourage sparsity of the model. Compared with the conventional maximum likelihood (ML) learning, the proposed SME algorithm enables DBN to be more unbiased to data distributions and robust to ...
How Jing, Yu Tsao
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Entropy for evaluation of Dempster-Shafer belief function models
International Journal of Approximate Reasoning, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Radim Jiroušek +2 more
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Monotonicity of Entropy Computations in Belief Functions
Intelligent Data Analysis, 1997This article addresses the issue of quantitative information measurement within the Dempster–Shafer belief function formalism. Entropy computation in Dempster–Shafer depends on the way uncertainty measures are conceptualized. However, freed of most probability constraints, uncertainty measures in Dempster–Shafer theory can lead to further advances in ...
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CALCULATING MAXIMUM-ENTROPY PROBABILITY DENSITIES FOR BELIEF FUNCTIONS
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 1994A common procedure for selecting a particular density from a given class of densities is to choose one with maximum entropy. The problem addressed here is this. Let S be a finite set and let B be a belief function on 2S. Then B induces a density on 2S, which in turn induces a host of densities on S.
AARON MEYEROWITZ +2 more
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2021 33rd Chinese Control and Decision Conference (CCDC), 2021
Huizi Cui, Bingyi Kang
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Huizi Cui, Bingyi Kang
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