Multi-Instance Learning by Treating Instances as Non-I.I.D. Samples
Abstract
Previous studies on multi-instance learning typically treated instances in the bags as independently and identically distributed. The instances in a bag, however, are rarely independent in real tasks, and a better performance can be expected if the instances are treated in an non-i.i.d. way that exploits relations among instances. In this paper, we propose two simple yet effective methods. In the first method, we explicitly map every bag to an undirected graph and design a graph kernel for distinguishing the positive and negative bags. In the second method, we implicitly construct graphs by deriving affinity matrices and propose an efficient graph kernel considering the clique information. The effectiveness of the proposed methods are validated by experiments.
Cite
Text
Zhou et al. "Multi-Instance Learning by Treating Instances as Non-I.I.D. Samples." International Conference on Machine Learning, 2009. doi:10.1145/1553374.1553534Markdown
[Zhou et al. "Multi-Instance Learning by Treating Instances as Non-I.I.D. Samples." International Conference on Machine Learning, 2009.](https://mlanthology.org/icml/2009/zhou2009icml-multi/) doi:10.1145/1553374.1553534BibTeX
@inproceedings{zhou2009icml-multi,
title = {{Multi-Instance Learning by Treating Instances as Non-I.I.D. Samples}},
author = {Zhou, Zhi-Hua and Sun, Yu-Yin and Li, Yufeng},
booktitle = {International Conference on Machine Learning},
year = {2009},
pages = {1249-1256},
doi = {10.1145/1553374.1553534},
url = {https://mlanthology.org/icml/2009/zhou2009icml-multi/}
}