A revised AdaBoost algorithm: FM-AdaBoost

In view of ensemble equivalence, this paper proposes a revised AdaBoost algorithm: FM-AdaBoost. It can ensure the ensemble error rates are the least by F-module, which filter classifiers after all of the iteration finish. At the same time, with the optional M-module it can ensure the training error rates decreases monotonously, which improves the training velocity effectively. In the end, simulation results show the algorithm is valid.

[1]  Yu-Jin Zhang,et al.  Fast Adaboost Training Algorithm by Dynamic Weight Trimming: Fast Adaboost Training Algorithm by Dynamic Weight Trimming , 2009 .

[2]  Stan Z. Li,et al.  FloatBoost learning and statistical face detection , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Harry Shum,et al.  Statistical Learning of Multi-view Face Detection , 2002, ECCV.

[4]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[5]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[6]  Weng Li-ping.  Face Detection Based on Modified AdaBoost Algorithm , 2012 .

[7]  Yujiang Jin.  Path Search Strategies for Handwritten Character String Recognition , 2009 .

[8]  Jia Hui,et al.  Fast Adaboost Training Algorithm by Dynamic Weight Trimming , 2009 .

[9]  Wang Ke,et al.  A Survey of Face Recognition Using Single Training Sample , 2008 .

[10]  Ding Xiaoqing,et al.  AdaBoost algorithm using multi-step correction , 2008 .

[11]  Yang Jing-yu,et al.  Fast Enhanced AdaBoost Algorithm Based on Dual-threshold , 2007 .

[12]  Harry Shum,et al.  FloatBoost Learning for Classification , 2002, NIPS.

[13]  Toniann Pitassi,et al.  A Gradient-Based Boosting Algorithm for Regression Problems , 2000, NIPS.

[14]  Peter L. Bartlett,et al.  Functional Gradient Techniques for Combining Hypotheses , 2000 .

[15]  Y. Freund,et al.  Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By , 2000 .

[16]  L. Shapley,et al.  Optimizing group judgmental accuracy in the presence of interdependencies , 1984 .