  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. For these disagreement-based approaches, there are several important
issues which still are unsolved, in this article we present theoretical
analyses to address these issues, which provides a theoretical foundation of
co-training and disagreement-based approaches.
