MIMLRBF: RBF neural networks for multi-instance multi-label learning |
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Authors: | Min-Ling Zhi-Jian |
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Affiliation: | aCollege of Computer and Information Engineering, Hohai University, Nanjing 210098, China |
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Abstract: | In multi-instance multi-label learning (MIML), each example is not only represented by multiple instances but also associated with multiple class labels. Several learning frameworks, such as the traditional supervised learning, can be regarded as degenerated versions of MIML. Therefore, an intuitive way to solve MIML problem is to identify its equivalence in its degenerated versions. However, this identification process would make useful information encoded in training examples get lost and thus impair the learning algorithm's performance. In this paper, RBF neural networks are adapted to learn from MIML examples. Connections between instances and labels are directly exploited in the process of first layer clustering and second layer optimization. The proposed method demonstrates superior performance on two real-world MIML tasks. |
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Keywords: | Machine learning Multi-instance multi-label learning Radial basis function Scene classification Text categorization |
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