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BMCBI
2010
224views more  BMCBI 2010»
11 years 1 months ago
An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data
Background: Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal ...
Susmita Datta, Vasyl Pihur, Somnath Datta
BMCBI
2011
10 years 5 months ago
To aggregate or not to aggregate high-dimensional classifiers
Background: High-throughput functional genomics technologies generate large amount of data with hundreds or thousands of measurements per sample. The number of sample is usually m...
Cheng-Jian Xu, Huub C. J. Hoefsloot, Age K. Smilde
ICPR
2004
IEEE
12 years 2 months ago
Nearest Neighbor Ensemble
Recent empirical work has shown that combining predictors can lead to significant reduction in generalization error. The individual predictors (weak learners) can be very simple, ...
Bojun Yan, Carlotta Domeniconi
VISSYM
2004
11 years 3 months ago
A Botanically Inspired High-Dimensional Visualization with Multivariate Glyphs
It is difficult for the average viewer to assimilate and comprehend huge amounts of high-dimensional data. It is important to present data in a way that allows the user a high lev...
Eleanor Boyle Chlan, Penny Rheingans
NIPS
2004
11 years 3 months ago
Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging
We study a method of optimal data-driven aggregation of classifiers in a convex combination and establish tight upper bounds on its excess risk with respect to a convex loss funct...
Vladimir Koltchinskii, Manel Martínez-Ram&o...
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