Results 21 to 30 of about 332,886 (299)
IEEE Access, 2020 Recent advances have illustrated substantial benefits from learning Bayesian networks (BNs). However, when the available data size is small, the BN parameter learning becomes a key challenge in many intelligent applications.
Yongyan Hou +4 more
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BN parameter learning based on improved QMAP algorithm under small data set conditions
Xi'an Gongcheng Daxue xuebao, 2023 Under the condition of Bayesian network (BN) small data set, the qualitative maximum a posteriori (QMAP) estimation tends to violate expert constraints, which causes the QMAP estimation to deviate the true value.
CHEN Haiyang +3 more
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Hard and Soft EM in Bayesian Network Learning from Incomplete Data
Algorithms, 2020 Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations.
Andrea Ruggieri +3 more
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Parameter Learning in ProbLog with Annotated Disjunctions
, 2022 In parameter learning, a partial interpretation most often contains information about only a subset of the parameters in the program. However, standard EM-based algorithms use all interpretations to learn all parameters, which significantly slows down ...
Yang, Wen-Chi +7 more
core +1 more source
Reply to determining structural identifiability of parameter learning machines [PDF]
, 2016 The paper Ran and Hu (2014, Neurocomputing) examines identifiability and parameter redundancy in classes of models used in machine learning. This note discusses the results on global identifiability and also clarifies that the paper's results on ...
Cole, Diana J.
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Efficient parameter learning of Bayesian network classifiers [PDF]
, 2017 Efficient parameter learning of Bayesian network ...
H De Sterck (15827969) +6 more
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Knowledge graph construction with structure and parameter learning for indoor scene design
Computational Visual Media, 2018 We consider the problem of learning a representation of both spatial relations and dependencies between objects for indoor scene design. We propose a novel knowledge graph framework based on the entity-relation model for representation of facts in indoor
Yuan Liang +4 more
doaj +1 more source
Process Monitoring Based on Multivariate Causality Analysis and Probability Inference
IEEE Access, 2018 System security is one of the key challenges of the cyber-physical systems. Bayesian approach can estimate and predict the potentially harmful factors of the general system, but it has many limitations that can lead to undesirable effects in the complex ...
Xiaolu Chen, Jing Wang, Jinglin Zhou
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IEEE Access, 2020 The sensor-based human activity recognition has been wildly applied in behavior tracking, health monitoring, indoor localization etc. Using activity continuity to assist activity recognition is an important research issue, in which the activity ...
Changhai Wang +5 more
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IEEE Access, 2022 Introducing parameter constraints has become a mainstream approach for learning Bayesian network parameters with small datasets. The QMAP (Qualitative Maximum a Posteriori) estimation has produced the best learning accuracy among existing learning ...
Ruohai Di +3 more
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