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BMCBI
2008
169views more  BMCBI 2008»
13 years 10 months ago
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification
Background: Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular sig...
Alexander R. Statnikov, Lily Wang, Constantin F. A...
ECML
2004
Springer
14 years 3 months ago
Improving Random Forests
Random forests are one of the most successful ensemble methods which exhibits performance on the level of boosting and support vector machines. The method is fast, robust to noise,...
Marko Robnik-Sikonja
PAMI
1998
127views more  PAMI 1998»
13 years 9 months ago
The Random Subspace Method for Constructing Decision Forests
—Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is ...
Tin Kam Ho
NIPS
2004
13 years 11 months ago
Using Random Forests in the Structured Language Model
In this paper, we explore the use of Random Forests (RFs) in the structured language model (SLM), which uses rich syntactic information in predicting the next word based on words ...
Peng Xu, Frederick Jelinek
ICIP
2009
IEEE
14 years 11 months ago
Age Regression From Faces Using Random Forests
Predicting the age of a person through face image analysis holds the potential to drive an extensive array of real world applications from human computer interaction and security ...