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ICDE
2011
IEEE
200views Database» more  ICDE 2011»
13 years 21 days ago
Deriving probabilistic databases with inference ensembles
— Many real-world applications deal with uncertain or missing data, prompting a surge of activity in the area of probabilistic databases. A shortcoming of prior work is the assum...
Julia Stoyanovich, Susan B. Davidson, Tova Milo, V...
CIKM
1997
Springer
14 years 1 months ago
Learning Belief Networks from Data: An Information Theory Based Approach
This paper presents an efficient algorithm for learning Bayesian belief networks from databases. The algorithm takes a database as input and constructs the belief network structur...
Jie Cheng, David A. Bell, Weiru Liu
BMCBI
2008
186views more  BMCBI 2008»
13 years 9 months ago
Variable selection for large p small n regression models with incomplete data: Mapping QTL with epistases
Background: Identifying quantitative trait loci (QTL) for both additive and epistatic effects raises the statistical issue of selecting variables from a large number of candidates...
Min Zhang, Dabao Zhang, Martin T. Wells
ASC
2004
13 years 8 months ago
Clustering terms in the Bayesian network retrieval model: a new approach with two term-layers
The retrieval performance of an information retrieval system usually increases when it uses the relationships among the terms contained in a given document collection. However, th...
Luis M. de Campos, Juan M. Fernández-Luna, ...
IFIP12
2008
13 years 10 months ago
P-Prism: A Computationally Efficient Approach to Scaling up Classification Rule Induction
Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unse...
Frederic T. Stahl, Max A. Bramer, Mo Adda