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ICDE
2011
IEEE
200views Database» more  ICDE 2011»
14 years 2 months 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
15 years 3 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»
14 years 11 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
14 years 11 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
15 years 20 days 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