  This paper proposes a universal method, Boost Picking, to train supervised
classification models mainly by un-labeled data. Boost Picking only adopts two
weak classifiers to estimate and correct the error. It is theoretically proved
that Boost Picking could train a supervised model mainly by un-labeled data as
effectively as the same model trained by 100% labeled data, only if recalls of
the two weak classifiers are all greater than zero and the sum of precisions is
greater than one. Based on Boost Picking, we present "Test along with Training
(TawT)" to improve the generalization of supervised models. Both Boost Picking
and TawT are successfully tested in varied little data sets.
