LAUSR

Multi-instance learning (MIL) plays an important role in many real applications, such as image recognition and text classification. The instance-based approach selects instances in each bag to train and has drawn significant attention recently. However, less work took the distribution information in the account and the margin distribution has been proven to be important to the generalization performance. In this paper, we propose an optimal representative distribution margin approach for multi-instance learning (MIORDM). The representative instances are the samples from the instance space and the distribution of them is important for us to find the best separation hyperplane. As the representative instances are selected iteratively, in each iteration, the instances will be more precise by the best hyperplane and the model will be better in the next iteration. In this way, a well-performed method can be derived with better generalization performance. Experiments compared with other types of state-of-the-art approaches on different datasets show that our method outperforms the others and achieves better generalization performance.