کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
410489 | 679146 | 2009 | 6 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: MIMLRBF: RBF neural networks for multi-instance multi-label learning MIMLRBF: RBF neural networks for multi-instance multi-label learning](/preview/png/410489.png)
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.
Journal: Neurocomputing - Volume 72, Issues 16–18, October 2009, Pages 3951–3956