A Multi-Instance Learning Algorithm Based on Normalized Radial Basis Function Network

Authors:
Yu-Mei Chai;Zhi-Wu Yang
Affiliations:
School of Information Engineering, Zhengzhou University, Zhengzhou 450052, Henan, China;School of Information Engineering, Zhengzhou University, Zhengzhou 450052, Henan, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

Multiple-Instance Learning is increasingly becoming one of the most promiscuous research areas in machine learning. In this paper, a new algorithm named NRBF-MI is proposed for Multi-Instance Learning based on normalized radial basis function network. This algorithm defined Compact Neighborhood of bags on which a new method is designed for training the network structure of NRBF-MI. The behavior of kernel function radius and its influence is analyzed. Furthermore a new kernel function is also defined for dealing with the labeled bags. Experimental results show that the NRBF-MI is a high efficient algorithm for Multi-Instance Learning.