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BMCBI
2008
186views more  BMCBI 2008»
15 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
ALGORITHMICA
2006
74views more  ALGORITHMICA 2006»
15 years 5 months ago
Parallelizing Feature Selection
Classification is a key problem in machine learning/data mining. Algorithms for classification have the ability to predict the class of a new instance after having been trained on...
Jerffeson Teixeira de Souza, Stan Matwin, Nathalie...
PAKDD
2005
ACM
114views Data Mining» more  PAKDD 2005»
15 years 11 months ago
Increasing Classification Accuracy by Combining Adaptive Sampling and Convex Pseudo-Data
The availability of microarray data has enabled several studies on the application of aggregated classifiers for molecular classification. We present a combination of classifier ag...
Chia Huey Ooi, Madhu Chetty
IBPRIA
2009
Springer
15 years 9 months ago
Textural Features for Hyperspectral Pixel Classification
Hyperspectral remote sensing provides data in large amounts from a wide range of wavelengths in the spectrum and the possibility of distinguish subtle differences in the image. For...
Olga Rajadell, Pedro García-Sevilla, Filibe...
PERCOM
2007
ACM
16 years 5 months ago
Structural Learning of Activities from Sparse Datasets
Abstract. A major challenge in pervasive computing is to learn activity patterns, such as bathing and cleaning from sensor data. Typical sensor deployments generate sparse datasets...
Fahd Albinali, Nigel Davies, Adrian Friday