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As a testament to their success, the theory of random forests has long been outpaced by their application in practice.
Classification and Regression Trees
L. Breiman, J. Friedman, C. Stone, and R. Olshen · 1984
Earlier work this paper cites.
A Probabilistic Theory of Pattern Recognition
L. Devroye, L. Györfi, and G. Lugosi · 1996
Earlier work this paper cites.
Mining high-speed data streams
P. Domingos and G. Hulten · 2000
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Random forests
L. Breiman · 2001
Earlier work this paper cites.
Online Bagging and Boosting
N. Oza and S. Russel · 2001
Earlier work this paper cites.
Random forests and adaptive nearest neighbors
Y. Lin and Y. Jeon · 2002
Earlier work this paper cites.
Random forest: a classification and regression tool for compound classification and QSAR modeling
V. Svetnik, A. Liaw, C. Tong, J. Culberson, R. Sheridan, and B. Feuston · 2003
Earlier work this paper cites.
Consistency for a Simple Model of Random Forests
L. Breiman · 2004
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Learning decision trees from dynamic data streams
J. Gama, P. Medas, and P. Rodrigues · 2005
Earlier work this paper cites.
Quantile regression forests
N. Meinshausen · 2006
Cited alongside, same era.
Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction
A. Prasad, L. Iverson, and A. Liaw · 2006
Cited alongside, same era.
Image classification using random forests and ferns
A. Bosch, A. Zisserman, and X. Munoz · 2007
Cited alongside, same era.
Random forests for classification in ecology
D. Cutler, T. Edwards, and K. Beard · 2007
Cited alongside, same era.
Streaming Random Forests
H. Abdulsalam · 2008
Cited alongside, same era.
Consistency of random forests and other averaging classifiers
G. Biau, L. Devroye, and G. Lugosi · 2008
Cited alongside, same era.
Risk bounds for purely uniformly random forests
R. Genuer · 2010
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Consistency of random survival forests
H. Ishwaran and U. Kogalur · 2010
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LIBSVM: A library for support vector machines
C. Chang and C. Lin · 2011
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Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning
A. Criminisi, J. Shotton, and E. Konukoglu · 2011
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Real-time human pose recognition in parts from single depth images
J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake · 2011
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Analysis of a Random Forests model
G. Biau · 2012
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A. Bifet, G. Holmes, and B. Pfahringer · 2009
Cited alongside, same era.
On-line random forests
A. Saffari, C. Leistner, J. Santner, M. Godec, and H. Bischof · 2009
Cited alongside, same era.
MOA: Massive Online Analysis, a framework for stream classification and clustering
A. Bifet, G. Holmes, and B. Pfahringer · 2010
Cited alongside, same era.
Ensembles of Restricted Hoeffding Trees
A. Bifet, E. Frank, G. Holmes, and B. Pfahringer · 2012
Later among the works it cites.
Variance reduction in purely random forests
R. Genuer · 2012
Later among the works it cites.