This project is devoted to create an easy and convenient Matlab based toolbox for investigations of AdaBoost based machine learning algorithms.
GML AdaBoost Matlab Toolbox is set of matlab functions and classes implementing a family of classification algorithms, known as Boosting.
So far we have implemented 3 different boosting schemes: Real AdaBoost, Gentle AdaBoost and Modest AdaBoost.
We have implemented a classification tree as a weak learner.
Alongside with 3 Boosting algorithms we also provide a class that should give you an easy way to make a crossvalidation test.
In 0.3 version of toolbox you can save constructed classifier to file and load it in your C++ application. C++ code for loading and using saved classifier is provided.
This toolbox was developed and implemented by Alexander Vezhnevets, who was at that time an undergraduate student of Lomonosov Moscow State University. If you have any questions or suggestions, please mail: email@example.com
 Y Freund and R. E. Schapire. Game theory, on-line prediction and boosting. In Proceedings of the Ninth Annual Conference on Computational Learning Theory, pages 325–332, 1996.
 R.E. Schapire and Y. Singer Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3):297-336, December 1999.
 Jerome Friedman, Trevor Hastie, and Robert Tibshirani. Additive logistic regression: A statistical view of boosting. The Annals of Statistics, 38(2):337–374, April 2000.
 P. Viola and M. Jones. Robust Real-time Object Detection. In Proc. 2nd Int'l Workshop on Statistical and Computational Theories of Vision -- Modeling, Learning, Computing and Sampling, Vancouver, Canada, July 2001.
 Alexander Vezhnevets, Vladimir Vezhnevets 'Modest AdaBoost' - Teaching AdaBoost to Generalize Better. Graphicon-2005, Novosibirsk Akademgorodok, Russia, 2005.
 Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.