Fetching the paper…
Reading the bibliography…
Multiple Additive Regression Trees (MART), an ensemble model of boosted regression trees, is known to deliver high prediction accuracy for diverse tasks, and it is widely used in practice.
A desicion-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1995
Earlier work this paper cites.
Random forests
Leo Breiman · 2001
Earlier work this paper cites.
An adaptive version of the boost by majority algorithm
Yoav Freund · 2001
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
Earlier work this paper cites.
Stochastic gradient boosting
Jerome H Friedman · 2002
Earlier work this paper cites.
Learning to rank using gradient descent
Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender · 2005
Earlier work this paper cites.
An empirical comparison of supervised learning algorithms
Rich Caruana and Alexandru Niculescu-Mizil · 2006
Cited alongside, same era.
Learning to rank with nonsmooth cost functions
Christopher J.C. Burges, Robert Ragno, and Quoc Viet Le · 2007
Cited alongside, same era.
A working guide to boosted regression trees
Jane Elith, John R Leathwick, and Trevor Hastie · 2008
Cited alongside, same era.
From ranknet to lambdarank to lambdamart: An overview
Christopher JC Burges · 2010
Cited alongside, same era.
Yahoo! learning to rank challenge overview
Olivier Chapelle and Yi Chang · 2011
Cited alongside, same era.
2d image registration in ct images using radial image descriptors
Franz Graf, Hans-Peter Kriegel, Matthias Schubert, Sebastian Pölsterl, and Alexander Cavallaro · 2011
Cited alongside, same era.
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov · 2012
Later among the works it cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Later among the works it cites.
UCI machine learning repository, 2013
Kevin Bache and Moshe Lichman · 2013
Later among the works it cites.
Learning with marginalized corrupted features
Laurens Maaten, Minmin Chen, Stephen Tyree, and Kilian Q. Weinberger · 2013
Later among the works it cites.
Dropout training as adaptive regularization
Stefan Wager, Sida Wang, and Percy Liang · 2013
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sida Wang and Christopher Manning · 2013
Later among the works it cites.