When semi-supervised learning meets ensemble learning

Abstract

Semi-supervised learning and ensemble learning are two important machine learning paradigms. The former attempts to achieve strong generalization by exploiting unlabeled data; the latter attempts to achieve strong generalization by using multiple learners. Although both paradigms have achieved great success during the past decade, they were almost developed separately. In this paper, we advocate that semi-supervised learning and ensemble learning are indeed beneficial to each other, and stronger learning machines can be generated by leveraging unlabeled data and classifier combination.

Key words： machine learning; semi-supervised learning; ensemble learning

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