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» Variable Selection for Optimal Decision Making
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JMLR
2006
92views more  JMLR 2006»
13 years 8 months ago
Linear Programs for Hypotheses Selection in Probabilistic Inference Models
We consider an optimization problem in probabilistic inference: Given n hypotheses Hj, m possible observations Ok, their conditional probabilities pk j, and a particular Ok, selec...
Anders Bergkvist, Peter Damaschke, Marcel Lüt...
BMCBI
2010
106views more  BMCBI 2010»
13 years 8 months ago
Selection of optimal reference genes for normalization in quantitative RT-PCR
Background: Normalization in real-time qRT-PCR is necessary to compensate for experimental variation. A popular normalization strategy employs reference gene(s), which may introdu...
Inna Chervoneva, Yanyan Li, Stephanie Schulz, Sean...
BMCBI
2007
174views more  BMCBI 2007»
13 years 8 months ago
Normalization method for metabolomics data using optimal selection of multiple internal standards
Background: Success of metabolomics as the phenotyping platform largely depends on its ability to detect various sources of biological variability. Removal of platform-specific so...
Marko Sysi-Aho, Mikko Katajamaa, Laxman Yetukuri, ...
GI
1998
Springer
14 years 24 days ago
Self-Organizing Data Mining
"KnowledgeMiner" was designed to support the knowledge extraction process on a highly automated level. Implemented are 3 different GMDH-type self-organizing modeling algo...
Frank Lemke, Johann-Adolf Müller
ESANN
2006
13 years 10 months ago
Random Forests Feature Selection with K-PLS: Detecting Ischemia from Magnetocardiograms
Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to AdaBoost and ...
Long Han, Mark J. Embrechts, Boleslaw K. Szymanski...