When Semi-supervised Learning Meets Ensemble Learning

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

• Type: Conference
• Year: 2009
• Where: MCS
• Authors: Zhi-Hua Zhou
• Keywords: Important Learning Paradigms, MCS 2009, Pattern Recognition, Strong Generalization, Unlabeled Data