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» Methods and Metrics for Selective Regression Testing
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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
METRICS
2003
IEEE
15 years 4 months ago
When Can We Test Less?
When it is impractical to rigorously assess all parts of complex systems, test engineers use defect detectors to focus their limited resources. In this article, we define some pr...
Tim Menzies, Justin S. Di Stefano, Kareem Ammar, K...
IJCNN
2008
IEEE
15 years 6 months ago
Sparse kernel density estimator using orthogonal regression based on D-Optimality experimental design
— A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experi...
Sheng Chen, Xia Hong, Chris J. Harris
ECAI
2010
Springer
14 years 9 months ago
Feature Selection by Approximating the Markov Blanket in a Kernel-Induced Space
The proposed feature selection method aims to find a minimum subset of the most informative variables for classification/regression by efficiently approximating the Markov Blanket ...
Qiang Lou, Zoran Obradovic
BMCBI
2010
150views more  BMCBI 2010»
14 years 9 months ago
Kernel based methods for accelerated failure time model with ultra-high dimensional data
Background: Most genomic data have ultra-high dimensions with more than 10,000 genes (probes). Regularization methods with L1 and Lp penalty have been extensively studied in survi...
Zhenqiu Liu, Dechang Chen, Ming Tan, Feng Jiang, R...