Theoretical Foundation of Co-Training and Disagreement-Based Algorithms

15 Aug 2017 Wei Wang Zhi-Hua Zhou

Disagreement-based approaches generate multiple classifiers and exploit the disagreement among them with unlabeled data to improve learning performance. Co-training is a representative paradigm of them, which trains two classifiers separately on two sufficient and redundant views; while for the applications where there is only one view, several successful variants of co-training with two different classifiers on single-view data instead of two views have been proposed... (read more)

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