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» Experimental Design for Variable Selection in Data Bases
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ISDA
2010
IEEE
14 years 10 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...
116
Voted
BMCBI
2006
130views more  BMCBI 2006»
15 years 15 days 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 9 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,...
ICLP
2009
Springer
16 years 1 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...
117
Voted
BMCBI
2010
150views more  BMCBI 2010»
14 years 10 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...