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» Incomplete-data classification using logistic regression
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ICML
2005
IEEE
14 years 5 months ago
Incomplete-data classification using logistic regression
A logistic regression classification algorithm is developed for problems in which the feature vectors may be missing data (features). Single or multiple imputation for the missing...
David Williams, Xuejun Liao, Ya Xue, Lawrence Cari...
BMCBI
2008
186views more  BMCBI 2008»
13 years 4 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
ICML
2007
IEEE
14 years 5 months ago
Trust region Newton methods for large-scale logistic regression
Large-scale logistic regression arises in many applications such as document classification and natural language processing. In this paper, we apply a trust region Newton method t...
Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi
ICML
2007
IEEE
14 years 5 months ago
Sparse probabilistic classifiers
The scores returned by support vector machines are often used as a confidence measures in the classification of new examples. However, there is no theoretical argument sustaining ...
Romain Hérault, Yves Grandvalet
ICPR
2008
IEEE
13 years 11 months ago
Locality preserving multi-nominal logistic regression
In this paper, we propose a novel algorithm of multi-nominal logistic regression in which the locality regularization term is introduced. The locality is defined by the neighborho...
Kenji Watanabe, Takio Kurita