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ICML
2009
IEEE

Multi-instance learning by treating instances as non-I.I.D. samples

11 years 5 months ago
Multi-instance learning by treating instances as non-I.I.D. samples
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.
Zhi-Hua Zhou, Yu-Yin Sun, Yu-Feng Li
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2009
Where ICML
Authors Zhi-Hua Zhou, Yu-Yin Sun, Yu-Feng Li
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