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AUSAI
2009
Springer
14 years 13 days ago
Ensemble Approach for the Classification of Imbalanced Data
Ensembles are often capable of greater prediction accuracy than any of their individual members. As a consequence of the diversity between individual base-learners, an ensemble wil...
Vladimir Nikulin, Geoffrey J. McLachlan, Shu-Kay N...
NCA
2006
IEEE
13 years 8 months ago
Analysing the localisation sites of proteins through neural networks ensembles
Scientists involved in the area of proteomics are currently seeking integrated, customised and validated research solutions to better expedite their work in proteomics analyses and...
Aristoklis D. Anastasiadis, George D. Magoulas
ECAI
2008
Springer
13 years 10 months ago
MTForest: Ensemble Decision Trees based on Multi-Task Learning
Many ensemble methods, such as Bagging, Boosting, Random Forest, etc, have been proposed and widely used in real world applications. Some of them are better than others on noisefre...
Qing Wang, Liang Zhang, Mingmin Chi, Jiankui Guo
NAR
2011
225views Computer Vision» more  NAR 2011»
12 years 11 months ago
Gramene database in 2010: updates and extensions
Now in its 10th year, the Gramene database (http:// www.gramene.org) has grown from its primary focus on rice, the first fully-sequenced grass genome, to become a resource for maj...
Ken Youens-Clark, Edward S. Buckler, Terry M. Cass...
ICDAR
2007
IEEE
14 years 2 months ago
Using Random Forests for Handwritten Digit Recognition
In the Pattern Recognition field, growing interest has been shown in recent years for Multiple Classifier Systems and particularly for Bagging, Boosting and Random Subspaces. Th...
Simon Bernard, Sébastien Adam, Laurent Heut...