Stan Z. Li, ZhenQiu Zhang, Heung-Yeung Shum, HongJiang Zhang  Home/Search
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Abstract: AdaBoost [3] minimizes an upper error bound which is an exponential function of the margin on the training set [14]. However, the ultimate goal in applications of pattern classification is always minimum error rate. On the other hand, AdaBoost needs an effective procedure for learning weak classifiers, which by itself is difficult especially for high dimensional data. In this paper, we present a novel procedure, called FloatBoost, for learning a better boosted classifier. FloatBoost uses... (Update)
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BibTeX entry: (Update)
S.Z. Li, Z.Q. Zhang, Harry Shum, and H.J. Zhang. FloatBoost learning for classification. In S. Thrun S. Becker and K. Obermayer, editors, NIPS 15. MIT Press, December 2002. http://citeseer.ist.psu.edu/li02floatboost.html More
@misc{ li02floatboost,
author = "S. Li and Z. Zhang and H. Shum and H. Zhang",
title = "FloatBoost learning for classification",
text = "S.Z. Li, Z.Q. Zhang, Harry Shum, and H.J. Zhang. FloatBoost learning for
classification. In S. Thrun S. Becker and K. Obermayer, editors, NIPS 15.
MIT Press, December 2002.",
year = "2002",
url = "citeseer.ist.psu.edu/li02floatboost.html" }
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