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» Using Sample Size to Limit Exposure to Data Mining
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JCS
2000
148views more  JCS 2000»
13 years 5 months ago
Using Sample Size to Limit Exposure to Data Mining
Data mining introduces new problems in database security. The basic problem of using non-sensitive data to infer sensitive data is made more difficult by the "prob abilistic&...
Chris Clifton
KDD
2006
ACM
149views Data Mining» more  KDD 2006»
14 years 6 months ago
Regularized discriminant analysis for high dimensional, low sample size data
Linear and Quadratic Discriminant Analysis have been used widely in many areas of data mining, machine learning, and bioinformatics. Friedman proposed a compromise between Linear ...
Jieping Ye, Tie Wang
BMCBI
2008
128views more  BMCBI 2008»
13 years 5 months ago
Nonparametric relevance-shifted multiple testing procedures for the analysis of high-dimensional multivariate data with small sa
Background: In many research areas it is necessary to find differences between treatment groups with several variables. For example, studies of microarray data seek to find a sign...
Cornelia Frömke, Ludwig A. Hothorn, Siegfried...
CVPR
2000
IEEE
14 years 7 months ago
High Dynamic Range Imaging: Spatially Varying Pixel Exposures
While real scenes produce a wide range of brightness variations, vision systems use low dynamic range image detectors that typically provide 8 bits of brightness data at each pixe...
Shree K. Nayar, Tomoo Mitsunaga
ICDM
2007
IEEE
254views Data Mining» more  ICDM 2007»
13 years 11 months ago
Sampling for Sequential Pattern Mining: From Static Databases to Data Streams
Sequential pattern mining is an active field in the domain of knowledge discovery. Recently, with the constant progress in hardware technologies, real-world databases tend to gro...
Chedy Raïssi, Pascal Poncelet