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» The Minimum-Entropy Set Cover Problem
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KDD
2008
ACM
140views Data Mining» more  KDD 2008»
14 years 9 months ago
Semi-supervised approach to rapid and reliable labeling of large data sets
Supervised classification methods have been shown to be very effective for a large number of applications. They require a training data set whose instances are labeled to indicate...
György J. Simon, Vipin Kumar, Zhi-Li Zhang
FGR
2004
IEEE
132views Biometrics» more  FGR 2004»
14 years 17 days ago
Expand Training Set for Face Detection by GA Re-sampling
Data collection for both training and testing a classifier is a tedious but essential step towards face detection and recognition. All of the statistical methods suffer from this ...
Jie Chen, Xilin Chen, Wen Gao
BMCBI
2008
175views more  BMCBI 2008»
13 years 9 months ago
Synonym set extraction from the biomedical literature by lexical pattern discovery
Background: Although there are a large number of thesauri for the biomedical domain many of them lack coverage in terms and their variant forms. Automatic thesaurus construction b...
John McCrae, Nigel Collier
SDM
2009
SIAM
175views Data Mining» more  SDM 2009»
14 years 6 months ago
Low-Entropy Set Selection.
Most pattern discovery algorithms easily generate very large numbers of patterns, making the results impossible to understand and hard to use. Recently, the problem of instead sel...
Hannes Heikinheimo, Jilles Vreeken, Arno Siebes, H...
APPROX
2009
Springer
195views Algorithms» more  APPROX 2009»
14 years 3 months ago
Approximating Node-Connectivity Augmentation Problems
The (undirected) Node Connectivity Augmentation (NCA) problem is: given a graph J = (V, EJ ) and connectivity requirements {r(u, v) : u, v ∈ V }, find a minimum size set I of n...
Zeev Nutov