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Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings.
A problem of dimensionality: A simple example
G. V. Trunk · 1979
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Induction of oblique decision trees
D. Heath, S. Kasif, and S. Salzberg · 1993
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A Probabilistic Theory of Pattern Recognition
L. Devroye, L. Gyorfi, and G. Lugosi · 1996
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Arcing classifier (with discussion and a rejoinder by the author)
Leo Breiman et al · 1998
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Random forests
L. Breiman · 2001
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Random projection in dimensionality reduction: applications to image and text data
Ella Bingham and Heikki Mannila · 2001
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Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
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Random Forests , 2002
Leo Breiman and Adele Cutler · 2002
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Random projection for high dimensional data clustering: A cluster ensemble approach
Xiaoli Z Fern and Carla E Brodley · 2003
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Experiments with random projections for machine learning
Dmitriy Fradkin and David Madigan · 2003
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Database-friendly random projections: Johnson-lindenstrauss with binary coins
Dimitris Achlioptas · 2003
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Variance and bias for general loss functions
Gareth M James · 2003
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An empirical comparison of supervised learning algorithms
Rich Caruana and Alexandru Niculescu-Mizil · 2006
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Very sparse random projections
P. Li, T. J. Hastie, and K. W. Church · 2006
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Rotation forest: A new classifier ensemble method
J. J. Rodriguez, L. I. Kuncheva, and C. J. Alonso · 2006
Cited alongside, same era.
An empirical evaluation of supervised learning in high dimensions
R. Caruana, N. Karampatziakis, and A. Yessenalina · 2008
Cited alongside, same era.
Random projections for manifold learning
Chinmay Hegde, Michael Wakin, and Richard Baraniuk · 2008
Cited alongside, same era.
Random projection trees and low dimensional manifolds
Sanjoy Dasgupta and Yoav Freund · 2008
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Consistency of random forests and other averaging classifiers
G. Biau, L. Devroye, and G. Lugosi · 2008
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Random projection trees for vector quantization
S Dasgupta and Y Freund · 2009
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Gérard Biau, Erwan Scornet, and Johannes Welbl · 2016
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Roflmao: Robust oblique forests with linear matrix operations
Tyler M Tomita, Mauro Maggioni, and Joshua T Vogelstein · 2017
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Explaining the success of adaboost and random forests as interpolating classifiers
A J Wyner, M Olson, J Bleich, and D Mease · 2017
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Lightgbm: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
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rerf: Randomer Forest , 2018
James Browne, Tyler Tomita, and Joshua T. Vogelstein · 2018
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Rcpp: Seamless R and C++ Integration , 2018
Dirk Eddelbuettel · 2018
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On oblique random forests
B. H. Menze, B.M Kelm, D. N. Splitthoff, U. Koethe, and F. A. Hamprecht · 2011
Cited alongside, same era.
Randomized partition trees for exact nearest neighbor search
S Dasgupta and K Sinha · 2013
Cited alongside, same era.
Do we need hundreds of classifiers to solve real world classification problems?
M. Fernandez-Delgado, E. Cernadas, S. Barro, and D. Amorim · 2014
Cited alongside, same era.
Understanding random forests: From theory to practice
Gilles Louppe · 2014
Cited alongside, same era.
Tom Rainforth and Frank Wood · 2015
Cited alongside, same era.
Fast and accurate head pose estimation via random projection forests
Donghoon Lee, Ming-Hsuan Yang, and Songhwai Oh · 2015
Cited alongside, same era.
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xgboost: Extreme Gradient Boosting , 2018
Tianqi Chen · 2018
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ranger: A fast Implementation of Random Forests , 2018
Marvin N. Wright · 2018
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Bohb: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
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High Dimensional Probability: An Introduction with Applications in Data Science
Roman Vershynin · 2019
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Tunability: Importance of hyperparameters of machine learning algorithms
Philipp Probst, Anne-Laure Boulesteix, and Bernd Bischl · 2019
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Big data in nanoscale connectomics, and the greed for training labels
Alessandro Motta, Meike Schurr, Benedikt Staffler, and Moritz Helmstaedter · 2019
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Forest Packing: Fast Parallel, Decision Forests
J Browne, D Mhembere, T Tomita, J Vogelstein, and R Burns · 2019
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