Statistical learning of multi-view face detection (2002)
| Venue: | In Proceedings of the 7th European Conference on Computer Vision |
| Citations: | 71 - 12 self |
BibTeX
@INPROCEEDINGS{Li02statisticallearning,
author = {Stan Z. Li and Long Zhu and Zhenqiu Zhang and Andrew Blake and Hongjiang Zhang and Harry Shum},
title = {Statistical learning of multi-view face detection},
booktitle = {In Proceedings of the 7th European Conference on Computer Vision},
year = {2002},
pages = {67--81}
}
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Abstract
1 Introduction Pattern recognition problems has two essential issues: (i) feature selection, and (ii) classifier design based on selected features. Boosting is a method which attempts to boost the accuracy of an ensemble of weak classifiers to a strong one. The AdaBoost algorithm [1] solved many of the practical difficulties of earlier boosting algorithms. Each weak classifier is trained one stage-wise to minimize the empirical error in a given distribution re-weighted according classification errors of the previously trained classifier. It is shown that AdaBoost is a sequential forward search procedure using the greedy selection strategy to minimize a certain margin on the training set [4].
Citations
| 505 | Example-based learning for view-based human face detection - Sung, Poggio - 1998 |
| 9 | Schapire, "A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting - Freund, E - 1997 |
| 1 | W.S.: "Boosting the margin: A new explanation for the effectiveness of voting methods". The Annals of Statistics 26 - Schapire, Freund, et al. - 1994 |
