Entropy-Based Learning of Compositional Models from Data

0546760 - ÚTIA 2022 RIV CH eng C - Konferenční příspěvek (zahraniční konf.)
Jiroušek, Radim - Kratochvíl, Václav - Shenoy, P. P.
Entropy-Based Learning of Compositional Models from Data.
Belief Functions: Theory and Applications - 6th International Conference, BELIEF 2021 - Proceedings. Cham: Springer, 2021 - (Denœux, T.; Lefèvre, E.; Liu, Z.; Pichon, F.), s. 117-126. Lecture Notes in Computer Science, 12915. ISBN 978-3-030-88600-4. ISSN 0302-9743. E-ISSN 1611-3349.
[International Conference on Belief Functions 2021 /6./. Shanghai (CN), 15.10.2021-19.10.2021]
Grant ostatní: GA ČR(CZ) GA19-06569S
Program: GA
Institucionální podpora: RVO:67985556
Klíčová slova: Compositional models * Entropy of Dempster-Shafer belief functions * Decomposable entropy of Dempster-Shafer belief functions
Obor OECD: Pure mathematics
http://library.utia.cas.cz/separaty/2021/MTR/jirousek-0546760.pdf

We investigate learning of belief function compositional models from data using information content and mutual information based on two different definitions of entropy proposed by Jiroušek and Shenoy in 2018 and 2020, respectively. The data consists of 2,310 randomly generated basic assignments of 26 binary variables from a pairwise consistent and decomposable compositional model. We describe results achieved by three simple greedy algorithms for constructing compositional models from the randomly generated low-dimensional basic assignments.
Trvalý link: http://hdl.handle.net/11104/0323760