Results 31 to 40 of about 3,284,626 (292)
On Causal Explanations in Bayesian Networks [PDF]
, 2007 Explanations in Bayesian networks are usually probabilistic measures of how well a hypothesis is supported by observations. This observational based approach does not fulfill all the properties one would expect from an explanation. In particular, it does
Nielsen, Ulf Holm
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Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee [PDF]
Pesquisa Agropecuária Brasileira, 2017 : The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica).
Gabi Nunes Silva +9 more
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Machine Learning based on Probabilistic Models Applied to Medical Data: The Case of Prostate Cancer
Journal of Innovation Information Technology and Application, 2023 The growth in the amount of data in companies puts analysts in difficulties when extracting hidden knowledge from data. Several models have emerged that focus on the notion of distances while ignoring the notion of conditional probability density.
Anaclet Tshikutu Bikengela +4 more
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Modeling Incomplete Knowledge of Semantic Web Using Bayesian Networks
Applied Artificial Intelligence, 2019 Interoperable ontologies already exist in the biomedical field, enabling scientists to communicate with minimum ambiguity. Unfortunately, ontology languages, in the semantic web, such as OWL and RDF(S), are based on crisp logic and thus they cannot ...
Messaouda Fareh
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CoRR, 2018 This paper describes and discusses Bayesian Neural Network (BNN). The paper showcases a few different applications of them for classification and regression problems. BNNs are comprised of a Probabilistic Model and a Neural Network. The intent of such a design is to combine the strengths of Neural Networks and Stochastic modeling.
Vikram Mullachery +2 more
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Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers
Complexity, 2018 Bayesian networks are useful machine learning techniques that are able to combine quantitative modeling, through probability theory, with qualitative modeling, through graph theory for visualization.
Gonzalo A. Ruz, Pamela Araya-Díaz
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GENERATIONS IN BAYESIAN NETWORKS
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 2019 This paper focuses on the study of some aspects of the theory of oriented graphs in Bayesian networks. In some papers on the theory of Bayesian networks, the concept of “Generation of vertices” denotes a certain set of vertices with many parents ...
Alexander Litvinenko +3 more
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Bayesian networks approach on intelligent system design for the diagnosis of heat exchanger
Jurnal Ilmiah SINERGI, 2022 The heat exchanger highly influences the series of cooling processes. Therefore, it is required to have maximum performance. Some of the factors causing a decrease in its performance are increased pressure drop in the Plate Heat Exchanger (PHE ...
Dedik Romahadi +4 more
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Bayesian generalized network design [PDF]
Theoretical Computer Science, 2020 25 pages, 0 figure. An extended abstract of this paper is to appear in the 27th Annual European Symposium on Algorithms (ESA 2019)
Yuval Emek +3 more
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Holistic Approach Promotes Failure Prevention of Smart Mining Machines Based on Bayesian Networks
Machines, 2023 In the forthcoming era of fully autonomous mining, spanning from drilling operations to port logistics, novel approaches will be essential to pre-empt hazardous situations in the absence of human intervention. The progression towards complete autonomy in
Madeleine Martinsen +3 more
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