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ICML
2007
IEEE
16 years 1 months ago
Parameter learning for relational Bayesian networks
We present a method for parameter learning in relational Bayesian networks (RBNs). Our approach consists of compiling the RBN model into a computation graph for the likelihood fun...
Manfred Jaeger
ISCAS
1999
IEEE
300views Hardware» more  ISCAS 1999»
15 years 4 months ago
Ripple correlation control, with some applications
Ripple correlation control is a nonlinear control approach applicable to power electronic circuits. It makes use of voltage, current, or power ripple and correlates this with swit...
P. T. Krein
CIBCB
2008
IEEE
15 years 7 months ago
Very large scale ReliefF for genome-wide association analysis
— The genetic causes of many monogenic diseases have already been discovered. However, most common diseases are actually the result of complex nonlinear interactions between mult...
Margaret J. Eppstein, Paul Haake
INFOCOM
2005
IEEE
15 years 6 months ago
Online time-constrained scheduling in linear networks
— We consider the problem of scheduling a sequence of packets over a linear network, where every packet has a source and a target, as well as a release time and a deadline by whi...
Joseph Naor, Adi Rosén, Gabriel Scalosub
ESWA
2008
162views more  ESWA 2008»
15 years 14 days ago
Proportionate flexible flow shop scheduling via a hybrid constructive genetic algorithm
The proportionate flow shop (PFS) is considered as a unique case of the flow shop problem in which the processing times of the operations belonging to the same job are equal. A pr...
Der-Fang Shiau, Shu-Chen Cheng, Yueh-Min Huang