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ICML
2000
IEEE

Learning Bayesian Networks for Diverse and Varying numbers of Evidence Sets

10 years 6 months ago
Learning Bayesian Networks for Diverse and Varying numbers of Evidence Sets
We introduce an expandable Bayesian network (EBN) to handle the combination of diverse multiple homogeneous evidence sets. An EBN is an augmented Bayesian network which instantiates its structure at runtime according to the structure of input. We show an application of an EBN for a multi-view 3-D object description problem in computer vision. The experiments show that the proposed method gives reasonable performance even for an unlearned structure of input data.
Zu Whan Kim, Ramakant Nevatia
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2000
Where ICML
Authors Zu Whan Kim, Ramakant Nevatia
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