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» Distributed adaptive sampling using bounded-errors
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ICDM
2009
IEEE
137views Data Mining» more  ICDM 2009»
14 years 2 months ago
A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks
This paper offers a local distributed algorithm for expectation maximization in large peer-to-peer environments. The algorithm can be used for a variety of well-known data mining...
Kanishka Bhaduri, Ashok N. Srivastava
BMCBI
2010
181views more  BMCBI 2010»
13 years 7 months ago
Intensity dependent estimation of noise in microarrays improves detection of differentially expressed genes
Background: In many microarray experiments, analysis is severely hindered by a major difficulty: the small number of samples for which expression data has been measured. When one ...
Amit Zeisel, Amnon Amir, Wolfgang J. Köstler,...
MANSCI
2007
90views more  MANSCI 2007»
13 years 7 months ago
Selecting a Selection Procedure
Selection procedures are used in a variety of applications to select the best of a finite set of alternatives. ‘Best’ is defined with respect to the largest mean, but the me...
Jürgen Branke, Stephen E. Chick, Christian Sc...
TNN
2010
127views Management» more  TNN 2010»
13 years 2 months ago
RAMOBoost: ranked minority oversampling in boosting
In recent years, learning from imbalanced data has attracted growing attention from both academia and industry due to the explosive growth of applications that use and produce imba...
Sheng Chen, Haibo He, Edwardo A. Garcia
EPS
1998
Springer
13 years 11 months ago
Acquisition of General Adaptive Features by Evolution
We investigate the following question. Do populations of evolving agents adapt only to their recent environment or do general adaptive features appear over time? We find statistica...
Dan Ashlock, John E. Mayfield