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CIDM
2009
IEEE
14 years 2 months ago
A new hybrid method for Bayesian network learning With dependency constraints
Abstract— A Bayes net has qualitative and quantitative aspects: The qualitative aspect is its graphical structure that corresponds to correlations among the variables in the Baye...
Oliver Schulte, Gustavo Frigo, Russell Greiner, We...
EMNLP
2006
13 years 8 months ago
Solving the Problem of Cascading Errors: Approximate Bayesian Inference for Linguistic Annotation Pipelines
The end-to-end performance of natural language processing systems for compound tasks, such as question answering and textual entailment, is often hampered by use of a greedy 1-bes...
Jenny Rose Finkel, Christopher D. Manning, Andrew ...
TKDE
2011
176views more  TKDE 2011»
13 years 2 months ago
Experience Transfer for the Configuration Tuning in Large-Scale Computing Systems
—This paper proposes a new strategy, the experience transfer, to facilitate the management of large-scale computing systems. It deals with the utilization of management experienc...
Haifeng Chen, Wenxuan Zhang, Guofei Jiang
BMCBI
2011
13 years 2 months ago
Using Stochastic Causal Trees to Augment Bayesian Networks for Modeling eQTL Datasets
Background: The combination of genotypic and genome-wide expression data arising from segregating populations offers an unprecedented opportunity to model and dissect complex phen...
Kyle C. Chipman, Ambuj K. Singh
NIPS
2008
13 years 9 months ago
Non-stationary dynamic Bayesian networks
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Joshua W. Robinson, Alexander J. Hartemink