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CIDM
2009
IEEE
15 years 4 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
14 years 11 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»
14 years 4 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
129
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BMCBI
2011
14 years 4 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
14 years 11 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