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» Informative sampling for large unbalanced data sets
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DRR
2009
13 years 5 months ago
Using synthetic data safely in classification
When is it safe to use synthetic data in supervised classification? Trainable classifier technologies require large representative training sets consisting of samples labeled with...
Jean Nonnemaker, Henry Baird
STOC
2007
ACM
112views Algorithms» more  STOC 2007»
14 years 8 months ago
Smooth sensitivity and sampling in private data analysis
We introduce a new, generic framework for private data analysis. The goal of private data analysis is to release aggregate information about a data set while protecting the privac...
Kobbi Nissim, Sofya Raskhodnikova, Adam Smith
JACM
2012
11 years 10 months ago
Continuous sampling from distributed streams
A fundamental problem in data management is to draw and maintain a sample of a large data set, for approximate query answering, selectivity estimation, and query planning. With la...
Graham Cormode, S. Muthukrishnan, Ke Yi, Qin Zhang
INFOVIS
1995
IEEE
13 years 11 months ago
The information mural: a technique for displaying and navigating large information spaces
Visualizations which depict entire information spaces provide context for navigation and browsing tasks; however, the limited size of the display screen makes creating effective g...
Dean F. Jerding, John T. Stasko
CVPR
2006
IEEE
13 years 11 months ago
Multiple Face Model of Hybrid Fourier Feature for Large Face Image Set
The face recognition system based on the only single classifier considering the restricted information can not guarantee the generality and superiority of performances in a real s...
Wonjun Hwang, Gyu-tae Park, Jong Ha Lee, Seok-Cheo...