Results 241 to 250 of about 672,665 (262)

Sparse maximum entropy deep belief nets

The 2013 International Joint Conference on Neural Networks (IJCNN), 2013
In 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., 2004
Summary: 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
openaire   +4 more sources

Monotonicity of Entropy Computations in Belief Functions

Intelligent Data Analysis, 1997
This 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, 1994
A 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, Fred Richman, Elbert A. Walker   +2 more
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A novel belief entropy and its application in cooperative situational awareness

Expert Systems With Applications
Xiaoyan Su, Danilo Pelusi
exaly   +4 more sources

A Decomposable Entropy of Belief Functions in the Dempster-Shafer Theory

2018
We 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
openaire   +2 more sources

Ordinal belief entropy

Soft Computing, 2023
Yuanpeng He, Yong Deng 0001
openaire   +1 more source

The generalized maximum belief entropy model

Soft Computing, 2022
Siran Li, Rui Cai 0001
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Degrees of belief, random worlds, and maximum entropy

2000
Consider 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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On cumulative belief entropy

2021 33rd Chinese Control and Decision Conference (CCDC), 2021
Huizi Cui, Bingyi Kang
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