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Maximum Entropy Learning with Deep Belief Networks [PDF]
Entropy, 2016 Conventionally, the maximum likelihood (ML) criterion is applied to train a deep belief network (DBN). We present a maximum entropy (ME) learning algorithm for DBNs, designed specifically to handle limited training data.
Payton Lin +4 more
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A Novel Uncertainty Management Approach for Air Combat Situation Assessment Based on Improved Belief Entropy [PDF]
Entropy, 2019 Uncertain information exists in each procedure of an air combat situation assessment. To address this issue, this paper proposes an improved method to address the uncertain information fusion of air combat situation assessment in the Dempster– ...
Ying Zhou, Yongchuan Tang, Xiaozhe Zhao
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Quantifying Epistemic Uncertainty in Multimodal Long-Tailed Classification: A Belief Entropy-Based Evidential Fusion Framework [PDF]
EntropyDeep multimodal learning has excelled in tasks involving vision, language, and audio modalities. Nevertheless, their performance on tail classes exhibits significant degradation under the long-tailed distributions common in real-world data, meanwhile ...
Guorui Zhu
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Belief Reliability Distribution Based on Maximum Entropy Principle
IEEE Access, 2018 Belief reliability is a new reliability metric based on the uncertainty theory, which aims to measure system performance incorporating the influences from design margin, aleatory uncertainty, and epistemic uncertainty.
Tianpei Zu +3 more
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Interval-valued belief entropies for Dempster–Shafer structures [PDF]
Soft Computing, 2021 In practical application problems, the uncertainty of an unknown object is often very difficult to accurately determine, so Yager proposed the interval-valued entropies for Dempster-Shafer structures, which is based on Dempster-Shafer structures and classic Shannon entropy and is an interval entropy model.
Yige Xue, Yong Deng 0001
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Conspiratorial Beliefs Observed through Entropy Principles [PDF]
Entropy, 2015 We propose a novel approach framed in terms of information theory and entropy to tackle the issue of the propagation of conspiracy theories. We represent the initial report of an event (such as the 9/11 terroristic attack) as a series of strings of information, each string classified by a two-state variable Ei = ±1, i = 1, …, N. If the values of the Ei
Golo, Nataša, Galam, Serge
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A New Correlation Measure for Belief Functions and Their Application in Data Fusion
Entropy, 2023 Measuring the correlation between belief functions is an important issue in Dempster–Shafer theory. From the perspective of uncertainty, analyzing the correlation may provide a more comprehensive reference for uncertain information processing.
Zhuo Zhang +3 more
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Mathematics, 2020 The Dempster–Shafer evidence theory has been widely applied in the field of information fusion. However, when the collected evidence data are highly conflicting, the Dempster combination rule (DCR) fails to produce intuitive results most of the time.
Dingyi Gan, Bin Yang, Yongchuan Tang
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Ordinal relative belief entropy
CoRR, 2021 Specially customised Entropies are widely applied in measuring the degree of uncertainties existing in the frame of discernment. However, all of these entropies regard the frame as a whole that has already been determined which dose not conform to actual situations.
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Maximum of Entropy for Belief Intervals Under Evidence Theory [PDF]
IEEE Access, 2020 The Dempster-Shafer Theory (DST) or Evidence Theory has been commonly used to deal with uncertainty. It is based on the basic probability assignment concept (BPA). The upper entropy on the credal set associated with a BPA is the only uncertainty measure in DST that verifies all the necessary mathematical properties and behaviors.
Serafín Moral-García +1 more
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