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Multi-instance learning and semi-supervised learning are different branches of machine learning. The former attempts to learn from a training set consists of labeled bags each containing many unlabeled instances; the latter tries to exploit abundant unlabeled instances when learning with a small number of labeled examples. In this paper, we establish a bridge between these two branches by showing that multi-instance learning can be viewed as a special case of semi-supervised learning. Based ondoi:10.1145/1273496.1273643 dblp:conf/icml/ZhouX07 fatcat:7d5n5b7bijh4vdo4ghdj3gci3u