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BMCBI
2008
186views more  BMCBI 2008»
13 years 5 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
APAQS
2000
IEEE
13 years 9 months ago
Software Quality Prediction Using Mixture Models with EM Algorithm
The use of the statistical technique of mixture model analysis as a tool for early prediction of fault-prone program modules is investigated. The Expectation-Maximum likelihood (E...
Ping Guo, Michael R. Lyu
IEICET
2007
114views more  IEICET 2007»
13 years 5 months ago
Analytic Optimization of Adaptive Ridge Parameters Based on Regularized Subspace Information Criterion
In order to obtain better learning results in supervised learning, it is important to choose model parameters appropriately. Model selection is usually carried out by preparing a ...
Shun Gokita, Masashi Sugiyama, Keisuke Sakurai
BMCBI
2008
132views more  BMCBI 2008»
13 years 5 months ago
Mixture models for analysis of melting temperature data
Background: In addition to their use in detecting undesired real-time PCR products, melting temperatures are useful for detecting variations in the desired target sequences. Metho...
Christoffer Nellåker, Fredrik Uhrzander, Joa...
AUSAI
2009
Springer
13 years 12 months ago
Enhancing MML Clustering Using Context Data with Climate Applications
Abstract. In Minimum Message Length (MML) clustering (unsupervised classification, mixture modelling) the aim is to infer a set of classes that best explains the observed data ite...
Gerhard Visser, David L. Dowe, Petteri Uotila