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» Incremental Bayesian networks for structure prediction
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IJAR
2008
119views more  IJAR 2008»
13 years 7 months ago
Adapting Bayes network structures to non-stationary domains
When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit observations, as they are read from a database, we call the process stru...
Søren Holbech Nielsen, Thomas D. Nielsen
ETFA
2008
IEEE
13 years 8 months ago
Efficient failure-free foundry production
Microshrinkages are known as probably the most difficult defects to avoid in high-precission foundry. Depending on the magnitude of this defect, the piece in which it appears must...
Yoseba K. Penya, Pablo Garcia Bringas, Argoitz Zab...
IWANN
2009
Springer
14 years 1 months ago
Optimising Machine-Learning-Based Fault Prediction in Foundry Production
Abstract. Microshrinkages are known as probably the most difficult defects to avoid in high-precision foundry. The presence of this failure renders the casting invalid, with the su...
Igor Santos, Javier Nieves, Yoseba K. Penya, Pablo...
PSB
2008
13 years 8 months ago
Integration of Microarray and Textual Data Improves the Prognosis Prediction of Breast, Lung, and Ovarian Cancer Patients
bstracts in the structure prior of a Bayesian network could improve the prediction of the prognosis in cancer. Our results show that prediction of the outcome with the text prior w...
O. Gaevert, Steven Van Vooren, Bart De Moor
BMCBI
2006
118views more  BMCBI 2006»
13 years 7 months ago
Predicting the effect of missense mutations on protein function: analysis with Bayesian networks
Background: A number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, ...
Chris J. Needham, James R. Bradford, Andrew J. Bul...