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
doaj +2 more sources
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
doaj +2 more sources
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
doaj +2 more sources
An Improved Multi-Source Data Fusion Method Based on the Belief Entropy and Divergence Measure [PDF]
Entropy, 2019 Dempster−Shafer (DS) evidence theory is widely applied in multi-source data fusion technology. However, classical DS combination rule fails to deal with the situation when evidence is highly in conflict.
Zhe Wang, Fuyuan Xiao
doaj +2 more sources
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
doaj +2 more sources
A new definition of entropy of belief functions in the Dempster–Shafer theory
International Journal of Approximate Reasoning, 2018 We propose a new definition of entropy for basic probability assignments (BPA) in the Dempster-Shafer (D-S) theory of belief functions, which is interpreted as a measure of total uncertainty in the BPA. Our definition is different from the definitions proposed by H¨ohle, Smets, Yager, Nguyen, Dubois-Prade, Lamata-Moral, Klir-Ramer, Klir-Parviz, Pal et ...
Prakash P. Shenoy, Radim Jirousek
exaly +4 more sources
A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion [PDF]
Sensors, 2017 Deyun Zhou +2 more
exaly +2 more sources
Conflict Management for Target Recognition Based on PPT Entropy and Entropy Distance
Energies, 2021 Conflicting evidence affects the final target recognition results. Thus, managing conflicting evidence efficiently can help to improve the belief degree of the true target.
Shijun Xu +5 more
doaj +1 more source
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
openaire +4 more sources
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
openaire +5 more sources