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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 0001
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Belief revision and information fusion on optimum entropy
Int. J. Intell. Syst., 2004Summary: This article presents new methods for probabilistic belief revision and information fusion. By making use of the information theoretical principles of optimum entropy (ME principles), we define a generalized revision operator that aims at simulating the human learning of lessons, and we introduce a fusion operator that handles probabilistic ...
Gabriele Kern-Isberner, Wilhelm Rödder
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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
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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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A Decomposable Entropy of Belief Functions in the Dempster-Shafer Theory
2018We define entropy of belief functions in the Dempster-Shafer (D-S) theory that satisfies a compound distributions property that is analogous to the property that characterizes Shannon’s definitions of entropy and conditional entropy for discrete probability distributions.
Radim Jirousek, Prakash P. Shenoy
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A Classification Tree Method Based on Belief Entropy for Evidential Data
2021Decision tree is widely applied in classification and recognition areas, but meanwhile it is hard to learn from evidential data with uncertainty. To solve this issue, we propose a decision tree method which can learn from uncertain data sets and guarantee a certain classification performance when handle problems with huge ignorance or uncertainty. This
Kangkai Gao, Liyao Ma, Yong Wang 0007
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The generalized maximum belief entropy model
Soft Computing, 2022Siran Li, Rui Cai 0001
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Degrees of belief, random worlds, and maximum entropy
2000Consider a doctor with a knowledge base KB consisting of first-order information (such as "All patients with hepatitis have jaundice"), statistical information (such as "80have hepatitis"), and default information (such as "patients with pneumonia typically have fever"). The doctor may want to make decisions regarding a particular patient, using the KB
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