Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Zhigao Guo"'
Publikováno v:
IEEE Access, Vol 10, Pp 37269-37280 (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 ap
Externí odkaz:
https://doaj.org/article/f396f21d33d84445b48f171de97a7a2a
Publikováno v:
IEEE Access, Vol 10, Pp 35768-35783 (2022)
Bearings are broadly applied in various types of industrial systems. Fault diagnosis, as a promising way for reliability of modern industrial internet of thing applications, has attracted increasing attention from both academia and industry fields. B
Externí odkaz:
https://doaj.org/article/d1307744e24e4067b4c720f5782c1ab5
Publikováno v:
Entropy, Vol 23, Iss 10, p 1283 (2021)
Maximum a posteriori estimation (MAP) with Dirichlet prior has been shown to be effective in improving the parameter learning of Bayesian networks when the available data are insufficient. Given no extra domain knowledge, uniform prior is often consi
Externí odkaz:
https://doaj.org/article/25f68ba578464c0e885be2a19bdefe3c
Autor:
Zhigao Guo, Anthony C. Constantinou
Publikováno v:
Entropy, Vol 22, Iss 10, p 1142 (2020)
Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning. Approximate solutions exist because exact learning is generally not applicable to networks o
Externí odkaz:
https://doaj.org/article/790538a21f3143df9a6c6c05a56f1611
Publikováno v:
International Journal of Approximate Reasoning. 151:292-321
Learning the structure of a Bayesian Network (BN) with score-based solutions involves exploring the search space of possible graphs and moving towards the graph that maximises a given objective function. Some algorithms offer exact solutions that gua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6376bfb714fdae5de39cf632946a7482
https://doi.org/10.1016/j.ijar.2022.09.016
https://doi.org/10.1016/j.ijar.2022.09.016
Publikováno v:
Artificial Intelligence Review.
Bayesian Networks (BNs) have become increasingly popular over the last few decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology, epidemiology, economics and the social sciences. This is especially true in real-w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3e459cdae8c53de7e54dbe6641100535
https://doi.org/10.1007/s10462-022-10351-w
https://doi.org/10.1007/s10462-022-10351-w
Publikováno v:
International Journal of Approximate Reasoning. 131:151-188
Numerous Bayesian Network (BN) structure learning algorithms have been proposed in the literature over the past few decades. Each publication makes an empirical or theoretical case for the algorithm proposed in that publication and results across stu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f856bffebcf7d18fcd4013d46a520397
https://doi.org/10.1016/j.ijar.2021.01.001
https://doi.org/10.1016/j.ijar.2021.01.001
Publikováno v:
Entropy
Volume 23
Issue 10
Entropy, Vol 23, Iss 1283, p 1283 (2021)
Volume 23
Issue 10
Entropy, Vol 23, Iss 1283, p 1283 (2021)
Maximum a posteriori estimation (MAP) with Dirichlet prior has been shown to be effective in improving the parameter learning of Bayesian networks when the available data are insufficient. Given no extra domain knowledge, uniform prior is often consi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::821064118b9fe61361d5e9033d7d3db3
http://europepmc.org/articles/PMC8534477
http://europepmc.org/articles/PMC8534477
Publikováno v:
2021 40th Chinese Control Conference (CCC).
Automatic pain assessment systems based on facial videos are consistently studied due to the demand of robust and cost-effective pain management. In order to improve the assessment accuracy under the dynamic and uncertain pain assessment environment,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0202718df673dfe50236df93b6ec0ceb
https://doi.org/10.23919/ccc52363.2021.9550304
https://doi.org/10.23919/ccc52363.2021.9550304
Publikováno v:
Pattern Recognition. 91:123-134
Purely data-driven methods often fail to learn accurate conditional probability table (CPT) parameters of discrete Bayesian networks (BNs) when training data are scarce or incomplete. A practical and efficient means of overcoming this problem is to i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dce1eb1e3e5d8315a610c8b8b9e5ae50
https://doi.org/10.1016/j.patcog.2019.02.006
https://doi.org/10.1016/j.patcog.2019.02.006