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BMCBI
2008
169views more  BMCBI 2008»
13 years 7 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...
KDD
2004
ACM
139views Data Mining» more  KDD 2004»
14 years 8 months ago
Learning a complex metabolomic dataset using random forests and support vector machines
Metabolomics is the omics science of biochemistry. The associated data include the quantitative measurements of all small molecule metabolites in a biological sample. These datase...
Young Truong, Xiaodong Lin, Chris Beecher
BMCBI
2008
101views more  BMCBI 2008»
13 years 7 months ago
Prediction of glycosylation sites using random forests
Background: Post translational modifications (PTMs) occur in the vast majority of proteins and are essential for function. Prediction of the sequence location of PTMs enhances the...
Stephen E. Hamby, Jonathan D. Hirst
AUSDM
2006
Springer
202views Data Mining» more  AUSDM 2006»
13 years 11 months ago
A Comparative Study of Classification Methods For Microarray Data Analysis
In response to the rapid development of DNA Microarray technology, many classification methods have been used for Microarray classification. SVMs, decision trees, Bagging, Boostin...
Hong Hu, Jiuyong Li, Ashley W. Plank, Hua Wang, Gr...
ICPR
2010
IEEE
14 years 20 hour ago
Fast and Spatially-Smooth Terrain Classification Using Monocular Camera
In this paper, we present a monocular camera based terrain classification scheme. The uniqueness of the proposed scheme is that it inherently incorporates spatial smoothness while...
Chetan Jakkoju, Madhava Krishna, C. V. Jawahar