CoTrade: Confident Co-Training With Data Editing

@article{Zhang2011CoTradeCC,
  title={CoTrade: Confident Co-Training With Data Editing},
  author={Min-Ling Zhang and Zhi-Hua Zhou},
  journal={IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)},
  year={2011},
  volume={41},
  pages={1612-1626}
}
Co-training is one of the major semi-supervised learning paradigms that iteratively trains two classifiers on two different views, and uses the predictions of either classifier on the unlabeled examples to augment the training set of the other. During the co-training process, especially in initial rounds when the classifiers have only mediocre accuracy, it is quite possible that one classifier will receive labels on unlabeled examples erroneously predicted by the other classifier. Therefore… CONTINUE READING