Semi-supervised learning and ensemble learning are two im- portant learning paradigms. The former attempts to achieve strong gen- eralization by exploiting unlabeled data; the latter attempts to achieve strong generalization by using multiple learners. In this paper we advo- cate generating stronger learning systems by leveraging unlabeled data and classiﬁer combination.
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