On incrementally using a small portion of strong unlabeled data for semi-supervised learning algorithms ?
We study enhancing the classification accuracy of semi-supervised algorithms. Simply recycled and incrementally reinforced selection strategies are compared. The work is done empirically when dealing with semi-supervised MarginBoost. The latter, albeit not always, outperforms the former for certain kinds of data.
semi-supervised learning incrementally reinforced selection strategy semi-supervised marginboost