  Semi-supervised wrapper methods are concerned with building effective
supervised classifiers from partially labeled data. Though previous works have
succeeded in some fields, it is still difficult to apply semi-supervised
wrapper methods to practice because the assumptions those methods rely on tend
to be unrealistic in practice. For practical use, this paper proposes a novel
semi-supervised wrapper method, Dual Teaching, whose assumptions are easy to
set up. Dual Teaching adopts two external classifiers to estimate the false
positives and false negatives of the base learner. Only if the recall of every
external classifier is greater than zero and the sum of the precision is
greater than one, Dual Teaching will train a base learner from partially
labeled data as effectively as the fully-labeled-data-trained classifier. The
effectiveness of Dual Teaching is proved in both theory and practice.
