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Reasoning over Bayesian Networks using Semantic Artificial Neural Networks

Batsakis Sotirios, Antoniou, Grigoris

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/169D34AA-AD65-4316-A28F-DBE0373BD650-
Αναγνωριστικόhttps://doi.org/10.1109/IISA52424.2021.9555501-
Αναγνωριστικόhttps://ieeexplore.ieee.org/document/9555501-
Γλώσσαen-
Μέγεθος4 pagesen
ΤίτλοςReasoning over Bayesian Networks using Semantic Artificial Neural Networksen
ΔημιουργόςBatsakis Sotiriosen
ΔημιουργόςΜπατσακης Σωτηριοςel
ΔημιουργόςAntoniou, Grigorisen
ΕκδότηςInstitute of Electrical and Electronics Engineersen
ΠερίληψηRepresentation of application domains, related concepts and their dependencies is often achieved using Bayesian Networks. In Bayesian Networks nodes represent random variables and arcs represent their dependencies. Since inference over Bayesian Networks is a complex task in this work a novel approach for representing and reasoning over Bayesian Networks using Semantically labeled Neural Networks is proposed and evaluated. Using Semantic Neural Networks combines advantages of Neural Networks such as wide adoption and highly optimized implementations while preserving the interpretability of Bayesian Networks which is an important requirement, especially in medical applications. In addition the proposed approach is evaluated over medical datasets with positive results.en
ΤύποςΔημοσίευση σε Συνέδριοel
ΤύποςConference Publicationen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2023-05-23-
Ημερομηνία Δημοσίευσης2021-
Θεματική ΚατηγορίαParameter estimationen
Θεματική ΚατηγορίαSemanticsen
Θεματική ΚατηγορίαEstimationen
Θεματική ΚατηγορίαArtificial neural networksen
Θεματική ΚατηγορίαMedical servicesen
Θεματική ΚατηγορίαMachine learningen
Θεματική ΚατηγορίαCognitionen
Βιβλιογραφική ΑναφοράS. Batsakis and G. Antoniou, "Reasoning over Bayesian Networks using Semantic Artificial Neural Networks," presented at the 2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA), Chania Crete, Greece, 2021, doi: 10.1109/IISA52424.2021.9555501.en

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