Computer Science – Learning
Scientific paper
2008-07-12
Computer Science
Learning
ICML, 2009
Scientific paper
Multi-instance learning attempts to learn from a training set consisting of labeled bags each containing many unlabeled instances. Previous studies typically treat the instances in the bags as independently and identically distributed. However, the instances in a bag are rarely independent, and therefore a better performance can be expected if the instances are treated in an non-i.i.d. way that exploits the relations among instances. In this paper, we propose a simple yet effective multi-instance learning method, which regards each bag as a graph and uses a specific kernel to distinguish the graphs by considering the features of the nodes as well as the features of the edges that convey some relations among instances. The effectiveness of the proposed method is validated by experiments.
Li Yu-Feng
Sun Yu-Yin
Zhou Zhi-Hua
No associations
LandOfFree
Multi-Instance Learning by Treating Instances As Non-I.I.D. Samples does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Multi-Instance Learning by Treating Instances As Non-I.I.D. Samples, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Multi-Instance Learning by Treating Instances As Non-I.I.D. Samples will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-12242