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» Experimental Design for Variable Selection in Data Bases
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ISDA
2010
IEEE
14 years 7 months ago
Feature selection is the ReliefF for multiple instance learning
Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature sel...
Amelia Zafra, Mykola Pechenizkiy, Sebastián...
BMCBI
2006
130views more  BMCBI 2006»
14 years 9 months ago
CARMA: A platform for analyzing microarray datasets that incorporate replicate measures
Background: The incorporation of statistical models that account for experimental variability provides a necessary framework for the interpretation of microarray data. A robust ex...
Kevin A. Greer, Matthew R. McReynolds, Heddwen L. ...
SDM
2009
SIAM
202views Data Mining» more  SDM 2009»
15 years 6 months ago
Proximity-Based Anomaly Detection Using Sparse Structure Learning.
We consider the task of performing anomaly detection in highly noisy multivariate data. In many applications involving real-valued time-series data, such as physical sensor data a...
Tsuyoshi Idé, Aurelie C. Lozano, Naoki Abe,...
143
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ICLP
2009
Springer
15 years 10 months ago
A Tabling Implementation Based on Variables with Multiple Bindings
Suspension-based tabling systems have to save and restore computation states belonging to OR branches. Stack freezing combined with (forward) trailing is among the better-known imp...
Pablo Chico de Guzmán, Manuel Carro, Manuel...
BMCBI
2010
150views more  BMCBI 2010»
14 years 7 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...