Multi-Instance Learning with Distribution Change


  • Wei-Jia Zhang Nanjing University
  • Zhi-Hua Zhou Nanjing University



Multi-instance learning deals with tasks where each example is a bag of instances, and the bag labels of training data are known whereas instance labels are unknown. Most previous studies on multi-instance learning assumed that the training and testing data are from the same distribution; however, this assumption is often violated in real tasks. In this paper, we present possibly the first study on multi-instance learning with distribution change. We propose the MICS approach by considering both bag-level and instance-level distribution change. Experiments show that MICS is almost always significantly better than many state-of-the-art multi-instance learning algorithms when distribution change occurs; and even when there is no distribution change, their performances are still comparable.




How to Cite

Zhang, W.-J., & Zhou, Z.-H. (2014). Multi-Instance Learning with Distribution Change. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).



Main Track: Novel Machine Learning Algorithms