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Abstract: A novel learning procedure, called FloatBoost, is proposed for learning a boosted classifier for achieving the minimum error rate. FloatBoost learning uses a backtrack mechanism after each iteration of AdaBoost learning to minimize the error rate directly, rather than minimizing an exponential function of the margin as in the traditional AdaBoost algorithms. A second contribution of the paper is a novel statistical model for learning best weak classifiers using a stagewise approximation of the... (Update)
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BibTeX entry: (Update)
S. Z. Li and Z. Zhang, "Floatboost learning and statistical face detection," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 26, pp. 1112-- 1123, September 2004. http://citeseer.ist.psu.edu/li04floatboost.html More
@article{ li04floatboost,
author = "Stan Z. Li and {ZhenQiu} Zhang",
title = "{FloatBoost} Learning and Statistical Face Detection",
journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
volume = "26",
number = "9",
year = "2004",
url = "citeseer.ist.psu.edu/li04floatboost.html" }
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