Fetching the paper…
Reading the bibliography…
This manuscript shows that AdaBoost and its immediate variants can produce approximate maximum margin classifiers simply by scaling step size choices with a fixed small constant.
Regression, prediction and shrinkage
Copas, J. B · 1983
Earlier work this paper cites.
A hard-core predicate for all one-way functions
Goldreich, Oded and Levin, Leonid · 1989
Earlier work this paper cites.
Cryptographic limitations on learning finite automata and boolean formulae
Kearns, Michael and Valiant, Leslie · 1989
Earlier work this paper cites.
Boosting a weak learning algorithm by majority
Freund, Yoav · 1995
Earlier work this paper cites.
Hard-core distributions for somewhat hard problems
Impagliazzo, Russell · 1995
Earlier work this paper cites.
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Yoav and Schapire, Robert E · 1997
Earlier work this paper cites.
Boosting the margin: A new explanation for the effectiveness of voting methods
Schapire, Robert E., Freund, Yoav, Barlett, Peter, and Lee, Wee Sun · 1997
Earlier work this paper cites.
Improved boosting algorithms using confidence-rated predictions
Schapire, Robert E. and Singer, Yoram · 1999
Earlier work this paper cites.
The OpenCV Library
Bradski, G · 2000
Earlier work this paper cites.
Greedy function approximation: A gradient boosting machine
Friedman, Jerome H · 2000
Cited alongside, same era.
Soft margins for adaboost
Rätsch, G., Onoda, T., and Müller, K.-R · 2001
Cited alongside, same era.
Logistic regression, AdaBoost and Bregman distances
Collins, Michael, Schapire, Robert E., and Singer, Yoram · 2002
Cited alongside, same era.
The dynamics of AdaBoost: cyclic behavior and convergence of margins
Rudin, Cynthia, Daubechies, Ingrid, and Schapire, Robert E · 2004
Cited alongside, same era.
The Cauchy-Schwarz Master Class
Steele, J. Michael · 2004
Cited alongside, same era.
Efficient margin maximizing with boosting
Rätsch, Gunnar and Warmuth, Manfred · 2005
Cited alongside, same era.
How boosting the margin can also boost classifier complexity
Reyzin, Lev and Schapire, Robert E · 2006
Later among the works it cites.
Totally corrective boosting algorithms that maximize the margin
Warmuth, Manfred K., Liao, Jun, and Rätsch, Gunnar · 2006
Later among the works it cites.
Analysis of boosting algorithms using the smooth margin function
Rudin, Cynthia, Schapire, Robert E., and Daubechies, Ingrid · 2007
Later among the works it cites.
On the equivalence of weak learnability and linear separability: New relaxations and efficient boosting algorithms
Shalev-Shwartz, Shai and Singer, Yoram · 2008
Later among the works it cites.
The convergence rate of AdaBoost
Mukherjee, Indraneel, Rudin, Cynthia, and Schapire, Robert · 2011
Later among the works it cites.
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zhang, Tong and Yu, Bin · 2005
Cited alongside, same era.
Numerical optimization
Nocedal, Jorge and Wright, Stephen J · 2006
Cited alongside, same era.
Later among the works it cites.
Boosting: Foundations and Algorithms
Schapire, Robert E. and Freund, Yoav · 2012
Later among the works it cites.
A primal-dual convergence analysis of boosting
Telgarsky, Matus · 2012
Later among the works it cites.