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ICML
2005
IEEE
14 years 8 months ago
Experimental comparison between bagging and Monte Carlo ensemble classification
Properties of ensemble classification can be studied using the framework of Monte Carlo stochastic algorithms. Within this framework it is also possible to define a new ensemble c...
Roberto Esposito, Lorenza Saitta
PR
2008
108views more  PR 2008»
13 years 7 months ago
From dynamic classifier selection to dynamic ensemble selection
In handwritten pattern recognition, the multiple classifier system has been shown to be useful for improving recognition rates. One of the most important tasks in optimizing a mul...
Albert Hung-Ren Ko, Robert Sabourin, Alceu de Souz...
ICIC
2009
Springer
14 years 2 months ago
Ensemble Classifiers Based on Kernel PCA for Cancer Data Classification
Now the classification of different tumor types is of great importance in cancer diagnosis and drug discovery. It is more desirable to create an optimal ensemble for data analysis ...
Jin Zhou, Yuqi Pan, Yuehui Chen, Yang Liu
INFFUS
2008
97views more  INFFUS 2008»
13 years 7 months ago
Using classifier ensembles to label spatially disjoint data
act 11 We describe an ensemble approach to learning from arbitrarily partitioned data. The partitioning comes from the distributed process12 ing requirements of a large scale simul...
Larry Shoemaker, Robert E. Banfield, Lawrence O. H...
BMCBI
2011
13 years 2 months ago
Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble
Background: Automated, image based high-content screening is a fundamental tool for discovery in biological science. Modern robotic fluorescence microscopes are able to capture th...
Bailing Zhang, Tuan D. Pham