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» Informative sampling for large unbalanced data sets
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BMCBI
2008
133views more  BMCBI 2008»
13 years 7 months ago
A Web-based and Grid-enabled dChip version for the analysis of large sets of gene expression data
Background: Microarray techniques are one of the main methods used to investigate thousands of gene expression profiles for enlightening complex biological processes responsible f...
Luca Corradi, Marco Fato, Ivan Porro, Silvia Scagl...
CVPR
2008
IEEE
14 years 2 months ago
Partitioning of image datasets using discriminative context information
We propose a new method to partition an unlabeled dataset, called Discriminative Context Partitioning (DCP). It is motivated by the idea of splitting the dataset based only on how...
Christoph H. Lampert
SDM
2010
SIAM
195views Data Mining» more  SDM 2010»
13 years 9 months ago
Adaptive Informative Sampling for Active Learning
Many approaches to active learning involve periodically training one classifier and choosing data points with the lowest confidence. An alternative approach is to periodically cho...
Zhenyu Lu, Xindong Wu, Josh Bongard
COLT
2004
Springer
14 years 1 months ago
Regularization and Semi-supervised Learning on Large Graphs
We consider the problem of labeling a partially labeled graph. This setting may arise in a number of situations from survey sampling to information retrieval to pattern recognition...
Mikhail Belkin, Irina Matveeva, Partha Niyogi
IVS
2002
106views more  IVS 2002»
13 years 7 months ago
Pixel bar charts: a visualization technique for very large multi-attribute data sets?
Simple presentation graphics are intuitive and easy-to-use, but show only highly aggregated data presenting only a very small number of data values (as in the case of bar charts) ...
Daniel A. Keim, Ming C. Hao, Umeshwar Dayal, Meich...