Minimum variance semi-supervised boosting for multi-label classification
Chenyang Zhao, Shaodan Zhai · 2015
We present a semi-supervised boosting algorithm for the multi-label classification by using the conditional label variance as a loss function over the unlabeled data. The experiments on the benchmark data sets show that the proposed algorithm outperforms its supervised counterpart as well as the existing information theoretic based semi-supervised methods, and its performance is steadily improving as more unlabeled data is available.