In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. Through analyzing two famous multi-instance learning algorithms, this paper shows that many supervised learning algorithms can be adapted to multi-instance learning, as long as their focuses are shifted from the discrimination on the instances to the discrimination on the bags. Moreover, considering that ensemble learning paradigms can effectively enhance supervised learners, this paper proposes to build ensembles of multi-instance learners to solve multi-instance problems. Experiments on a real-world benchmark test show that ensemble learning paradigms can significantly enhance multi-instance learners, and the result achieved by EM-DD ensemble exceeds the best result on the benchmark test reported in literature.