Stan Li, Zhenqiu Zhang, Heung-yeung Shum, Hongjiang Zhang
AdaBoost  minimizes an upper error bound which is an exponential function of the margin on the training set . 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 a backtrack mechanism after each iteration of AdaBoost to remove weak classiﬁers which cause higher error rates. The resulting ﬂoat-boosted classiﬁer consists of fewer weak classiﬁers yet achieves lower error rates than AdaBoost in both training and test. We also propose a statistical model for learning weak classiﬁers, based on a stagewise approximation of the posterior using an overcomplete set of scalar features. Experi- mental comparisons of FloatBoost and AdaBoost are provided through a difﬁcult classiﬁcation problem, face detection, where the goal is to learn from training examples a highly nonlinear classiﬁer to differentiate be- tween face and nonface patterns in a high dimensional space. The results clearly demonstrate the promises made by FloatBoost over AdaBoost.