On the relation between multi-instance learning and semi-supervised learning

Zhi-Hua Zhou, Jun-Ming Xu
2007 Proceedings of the 24th international conference on Machine learning - ICML '07  
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 on
more » ... his recognition, we propose the MissSVM algorithm which addresses multi-instance learning using a special semisupervised support vector machine. Experiments show that solving multi-instance problems from the view of semi-supervised learning is feasible, and the MissSVM algorithm is competitive with state-of-the-art multiinstance learning algorithms.
doi:10.1145/1273496.1273643 dblp:conf/icml/ZhouX07 fatcat:7d5n5b7bijh4vdo4ghdj3gci3u