Adaboost, which stands for adaptive boost, is a powerful classification techniques, by utilizing multiple weak classifiers. It implies inner connection with SVM. In some recent papers. It has been used for feature selection of multiple person tracking related algorithms.
I, myself, have known about this algorithm for a very long time, since the class of pattern recognition, (taught by Prof. Jennifer Dy in Northeastern University). However, it has always been a mystery for me since then. However, today, I found an excellent tutorial. (A Short Introduction to Boosting, http://www.yorku.ca/gisweb/eats4400/boost.pdf)
The tutorial could be a good supplementary materials for students to read. Before or after reading the tutorial, I would recommend people to read Adaboost documents for OpenCV, which is a open Computer Vision Library. This documents as a contact version of the Adaboost algorithm, with a decent implementation. The link is as following: http://opencv.willowgarage.com/documentation/cpp/boosting.html
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