A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Detection via Soft Cascade
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Linear Asymmetric Classifier for cascade detectors
ICML '05 Proceedings of the 22nd international conference on Machine learning
Robustifying AdaBoost by Adding the Naive Error Rate
Neural Computation
Pareto optimal linear classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Feature-centric evaluation for efficient cascaded object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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A novel variant of AdaBoost named AcoBoost is proposed to directly solve the asymmetric constraint optimization problem for cascade face detector using a two-stage feature selection approach. In the first stage, many candidate features are picked out by minimizing the weighted error. In the second stage, the optimal feature is singled out by minimizing the asymmetric constraint error. By doing so, the convergence rate is greatly speeded up. Besides, a new sample set called selection set is added into AcoBoost to prevent overfitting on the training set, which ensures good enough generalization ability for AcoBoost. The experimental results on building several upright frontal cascade face detectors show that the AcoBoost based classifiers have much better convergence ability and slightly worse generalization ability than the AdaBoost based ones. Some AcoBoost based cascade face detectors have satisfactory performance on the CMU+MIT upright frontal face test set.