Chenguang Wang, Yangqiu Song, et al.
ACM TKDD
In this paper, we present a homotopy regularization algorithm for boosting. We introduce a regularization term with adaptive weight into the boosting framework and compose a homotopy objective function. Optimization of this objective approximately composes a solution path for the regularized boosting. Following this path, we can find suitable solution efficiently using early stopping. Experiments show that this adaptive regularization method gives a more efficient parameter selection strategy than regularized boosting and semi-supervised boosting algorithms, and significantly improves the performances of traditional AdaBoost and related methods. © 2010 IEEE.
Chenguang Wang, Yangqiu Song, et al.
ACM TKDD
Rita Chattopadhyay, Zheng Wang, et al.
KDD 2012
Chenguang Wang, Yangqiu Song, et al.
CIKM 2017
Fei Wang, Bin Zhao, et al.
IEEE Transactions on Neural Networks