Multi-instance learning with relational information of instances
Gunawan Herman, Getian Ye, Yang Wang, Jie Xu, Bang Zhang · 2009
Multi-instance learning (MIL) has many applications, including image and text categorization. One of the most effective approaches to MIL is by using support vector machines with multi-instance kernels. In this paper we propose a multi-instance kernel, called MIR-kernel, that takes into account the relational information of instances when computing similarities between bags. The relational information of instances are derived from the statistics of the distances between instances in feature space. The aim of MIR-kernel is to efficiently capture the context in which instances occur within bags, so that it is able to better compute the similarities between bags. Experimental results on image and text categorization demonstrate the effectiveness of the proposed method compared to other methods.