FloatBoost learning and statistical face detection

作者: S.Z. Li , Zhenqiu Zhang

DOI: 10.1109/TPAMI.2004.68

关键词:

摘要: … Search into boosting learning allows deletions of weak … the AdaBoost learning algorithm, in the notion of RealBoost [4], [10], … about 20 strong classifiers, and has a detection rate of about …

参考文章(47)
Umesh V. Vazirani, Michael J. Kearns, An Introduction to Computational Learning Theory ,(1994)
Stan Z. Li, Long Zhu, ZhenQiu Zhang, Andrew Blake, HongJiang Zhang, Harry Shum, Statistical Learning of Multi-view Face Detection european conference on computer vision. pp. 67- 81 ,(2002) , 10.1007/3-540-47979-1_5
Peter Bartlett, M Frean, Llew Mason, Jon Baxter, Functional Gradient Techniques for Combining Hypotheses MIT Press. pp. 221- 246 ,(2000)
Handbook of Face Recognition Springer-Verlag New York, Inc.. ,(2011) , 10.1007/978-0-85729-932-1
Patrice Y. Simard, Yann A. LeCun, John S. Denker, Bernard Victorri, Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation neural information processing systems. ,vol. 1524, pp. 239- 274 ,(1998) , 10.1007/978-3-642-35289-8_17
Francois Fleuret, Donald Geman, Coarse-to-Fine Face Detection International Journal of Computer Vision. ,vol. 41, pp. 85- 107 ,(2001) , 10.1023/A:1011113216584
Jerome H. Friedman, Greedy function approximation: A gradient boosting machine. Annals of Statistics. ,vol. 29, pp. 1189- 1232 ,(2001) , 10.1214/AOS/1013203451
Yongmin Li, Shaogang Gong, H. Liddell, Support vector regression and classification based multi-view face detection and recognition ieee international conference on automatic face and gesture recognition. pp. 300- 305 ,(2000) , 10.1109/AFGR.2000.840650
Robert E. Schapire, Yoav Freund, Peter Bartlett, Wee Sun Lee, Boosting the margin: a new explanation for the effectiveness of voting methods Annals of Statistics. ,vol. 26, pp. 1651- 1686 ,(1998) , 10.1214/AOS/1024691352
Yoav Freund, Robert E Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting conference on learning theory. ,vol. 55, pp. 119- 139 ,(1997) , 10.1006/JCSS.1997.1504