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Boosting is a generic learning method for classification and regression.
An algorithm for quadratic programming
Marguerite Frank and Philip Wolfe · 1956
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Some comments on wolfe’s ‘away step’
Jacques Guélat and Patrice Marcotte · 1986
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1997
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Boosting the margin: A new explanation for the effectiveness of voting methods
Robert E Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee · 1998
Earlier work this paper cites.
Boosting in the limit: Maximizing the margin of learned ensembles
Adam J Grove and Dale Schuurmans · 1998
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Prediction games and arcing algorithms
Leo Breiman · 1999
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Functional gradient techniques for combining hypotheses
Llew Mason, Jonathan Baxter, Peter L Bartlett, and Marcus Frean · 1999
Earlier work this paper cites.
Additive logistic regression: a statistical view of boosting
Jerome Friedman, Trevor Hastie, Robert Tibshirani, et al · 2000
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On the lasso and its dual
Michael R Osborne, Brett Presnell, and Berwin A Turlach · 2000
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
Cited alongside, same era.
Consistency for l2boosting and matching pursuit with trees and tree-type basis functions
Peter Lukas Buhlmann · 2002
Cited alongside, same era.
Stochastic gradient boosting
Jerome H Friedman · 2002
Cited alongside, same era.
Empirical margin distributions and bounding the generalization error of combined classifiers
Vladimir Koltchinskii and Dmitry Panchenko · 2002
Cited alongside, same era.
The boosting approach to machine learning: An overview
Robert E Schapire · 2003
Cited alongside, same era.
On the bayes-risk consistency of regularized boosting methods
Gábor Lugosi and Nicolas Vayatis · 2004
Cited alongside, same era.
The elements of statistical learning
Trevor Hastie, Robert Tibshirani, Jerome Friedman, T Hastie, J Friedman, and R Tibshirani · 2009
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Totally corrective boosting for regularized risk minimization
Chunhua Shen, Hanxi Li, and Nick Barnes · 2010
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Trading accuracy for sparsity in optimization problems with sparsity constraints
Shai Shalev-Shwartz, Nathan Srebro, and Tong Zhang · 2010
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Coresets, sparse greedy approximation, and the frank-wolfe algorithm
Kenneth L Clarkson · 2010
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2012
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Boosting: Foundations and algorithms
Robert E Schapire and Yoav Freund · 2012
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Boosted lasso
Peng Zhao and Bin Yu · 2004
Cited alongside, same era.
Boosting with early stopping: convergence and consistency
Tong Zhang and Bin Yu · 2005
Cited alongside, same era.
Boosting with structural sparsity
John Duchi and Yoram Singer · 2009
Cited alongside, same era.
Speed and sparsity of regularized boosting
Yongxin T Xi, Zhen J Xiang, Peter J Ramadge, and Robert E Schapire · 2009
Cited alongside, same era.
Later among the works it cites.
Revisiting frank-wolfe: Projection-free sparse convex optimization
Martin Jaggi · 2013
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The frank-wolfe algorithm: New results, and connections to statistical boosting
Paul Grigas, Robert Freund, and Rahul Mazumder · 2013
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UCI machine learning repository, 2013
M. Lichman · 2013
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