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» Feature Subset Selection Using a Genetic Algorithm
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BIOINFORMATICS
2006
92views more  BIOINFORMATICS 2006»
15 years 2 months ago
What should be expected from feature selection in small-sample settings
Motivation: High-throughput technologies for rapid measurement of vast numbers of biological variables offer the potential for highly discriminatory diagnosis and prognosis; howev...
Chao Sima, Edward R. Dougherty
ICPR
2008
IEEE
15 years 8 months ago
Ranking the local invariant features for the robust visual saliencies
Local invariant feature based methods have been proven to be effective in computer vision for object recognition and learning. But for an image, the number of points detected and ...
Shengping Xia, Peng Ren, Edwin R. Hancock
BMCBI
2004
158views more  BMCBI 2004»
15 years 2 months ago
Incremental genetic K-means algorithm and its application in gene expression data analysis
Background: In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms suc...
Yi Lu, Shiyong Lu, Farshad Fotouhi, Youping Deng, ...
SIGCSE
2004
ACM
101views Education» more  SIGCSE 2004»
15 years 7 months ago
Effective features of algorithm visualizations
Many algorithm visualizations have been created, but little is known about which features are most important to their success. We believe that pedagogically useful visualizations ...
Purvi Saraiya, Clifford A. Shaffer, D. Scott McCri...
INFORMS
1998
100views more  INFORMS 1998»
15 years 1 months ago
Feature Selection via Mathematical Programming
The problem of discriminating between two nite point sets in n-dimensional feature space by a separating plane that utilizes as few of the features as possible, is formulated as a...
Paul S. Bradley, Olvi L. Mangasarian, W. Nick Stre...