Entropy-Based Learning of Compositional Models from Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00546760" target="_blank" >RIV/67985556:_____/21:00546760 - isvavai.cz</a>
Alternative codes found
RIV/61384399:31160/21:00057557
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-88601-1_12" target="_blank" >http://dx.doi.org/10.1007/978-3-030-88601-1_12</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-88601-1_12" target="_blank" >10.1007/978-3-030-88601-1_12</a>

Result language
angličtina
Original language name
Entropy-Based Learning of Compositional Models from Data
Original language description
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.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Belief Functions: Theory and Applications - 6th International Conference, BELIEF 2021 - Proceedings
ISBN
978-3-030-88600-4
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
10
Pages from-to
117-126
Publisher name
Springer
Place of publication
Cham
Event location
Shanghai
Event date
Oct 15, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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