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» The Pipelined Set Cover Problem
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KDD
2008
ACM
140views Data Mining» more  KDD 2008»
14 years 10 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 1 months 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 10 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
SIAMCOMP
1998
114views more  SIAMCOMP 1998»
13 years 9 months ago
Maximum k-Chains in Planar Point Sets: Combinatorial Structure and Algorithms
A chain of a set P of n points in the plane is a chain of the dominance order on P. A k-chain is a subset C of P that can be covered by k chains. A k-chain C is a maximum k-chain ...
Stefan Felsner, Lorenz Wernisch
SDM
2009
SIAM
175views Data Mining» more  SDM 2009»
14 years 7 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...