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IJCAI
2007
15 years 1 months ago
A Theoretical Framework for Learning Bayesian Networks with Parameter Inequality Constraints
The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of t...
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat ...
IJCAI
2003
15 years 1 months ago
When Discriminative Learning of Bayesian Network Parameters Is Easy
Bayesian network models are widely used for discriminative prediction tasks such as classification. Usually their parameters are determined using 'unsupervised' methods ...
Hannes Wettig, Peter Grünwald, Teemu Roos, Pe...
CSE
2009
IEEE
15 years 6 months ago
Self-Tuning the Parameter of Adaptive Non-linear Sampling Method for Flow Statistics
—Flow statistics is a basic task of passive measurement and has been widely used to characterize the state of the network. Adaptive Non-Linear Sampling (ANLS)is one of the most a...
Chengchen Hu, Bin Liu
APSCC
2008
IEEE
15 years 6 months ago
Runtime Prediction Based Grid Scheduling of Parameter Sweep Jobs
—This paper examines the problem of predicting job runtimes by exploiting the properties of parameter sweeps. A new parameter sweep prediction framework GIPSy (Grid Information P...
Sam Verboven, Peter Hellinckx, Frans Arickx, Jan B...
SEMCCO
2010
14 years 9 months ago
Differential Evolution Algorithm with Ensemble of Parameters and Mutation and Crossover Strategies
Differential Evolution (DE) has attracted much attention recently as an effective approach for solving numerical optimization problems. However, the performance of DE is sensitive ...
Rammohan Mallipeddi, Ponnuthurai Nagaratnam Sugant...