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» Variable selection using random forests
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CSDA
2008
84views more  CSDA 2008»
14 years 9 months ago
Bayesian spatial prediction of the site index in the study of the Missouri Ozark Forest Ecosystem Project
This paper presents a Bayesian spatial method for analysing the site index data from the Missouri Ozark Forest Ecosystem Project (MOFEP). Based on ecological background and availa...
Xiaoqian Sun, Zhuoqiong He, John Kabrick
79
Voted
CVPR
2006
IEEE
15 years 11 months ago
AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition
Activity recognition is an important issue in building intelligent monitoring systems. We address the recognition of multilevel activities in this paper via a conditional Markov r...
Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh, ...
CATA
2008
14 years 11 months ago
Investigation of Random Forest Performance with Cancer Microarray Data
The diagnosis of cancer type based on microarray data offers hope that cancer classification can be highly accurate for clinicians to choose the most appropriate forms of treatmen...
Myungsook Klassen, Matt Cummings, Griselda Saldana
77
Voted
BMCBI
2008
169views more  BMCBI 2008»
14 years 9 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...
CSDA
2004
188views more  CSDA 2004»
14 years 9 months ago
A bandwidth selection for kernel density estimation of functions of random variables
In this investigation, the problem of estimating the probability density function of a function of m independent identically distributed random variables, g(X1, X2, ..., Xm) is co...
A. R. Mugdadi, Ibrahim A. Ahmad