When semi-supervised learning meets ensemble learning

  • Zhou Z
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Abstract

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.

Author-supplied keywords

  • Ensemble learning
  • Machine learning
  • Semi-supervised learning

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Authors

  • Zhi Hua Zhou

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