Entropy-Based Learning of Compositional Models from Data-Reference-Cited by-同舟云学术

Entropy-Based Learning of Compositional Models from Data

Published:2021 Issue: Volume: Page:117-126
ISSN:0302-9743
Container-title:Belief Functions: Theory and Applications
language:
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Author:

Jiroušek Radim^ORCID,Kratochvíl Václav^ORCID,Shenoy Prakash P.^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-88601-1_12

Reference16 articles.

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3. Jiroušek, R.: Composition of probability measures on finite spaces. In: Geiger, D., Shenoy, P.P. (eds.) Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI 1997), pp. 274–281. Morgan Kaufmann (1997)

4. Jiroušek, R.: Foundations of compositional model theory. Int. J. Gener. Syst. 40(6), 623–678 (2011)

5. Jiroušek, R.: On two composition operators in Dempster-Shafer theory. In: Augustin, T., Doria, S., Miranda, E., Quaeghebeur, E. (eds.) Proceedings of the 9th International Symposium on Imprecise Probability: Theories and Applications (ISIPTA 2015), pp. 157–165. Society for Imprecise Probability: Theories and Applications (2015)

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1. Entropy for evaluation of Dempster-Shafer belief function models;International Journal of Approximate Reasoning;2022-12

2. Measuring Quality of Belief Function Approximations;Lecture Notes in Computer Science;2022

3. Approximations of Belief Functions Using Compositional Models;Lecture Notes in Computer Science;2021