Abstract
Multi-instance multi-label learning (MIML) is an innovative learning framework where each sample is represented by multiple instances and associated with multiple class labels. In several learning situations, the multi-instance multi-label RBF neural networks (MIMLRBF) can exploit connections between the instances and the labels of an MIML example directly, while most of other algorithms cannot learn that directly. However, the singular value decomposition (SVD) method used to compute the weights of the output layer will cause augmented overall error in network performance when training data are noisy or not easily discernible. This paper presents an improved approach to learning algorithms used for training MIMLRBF. The steepest descent (SD) method is used to optimize the weights after they are initialized by the SVD method. Comparing results employing diverse learning strategies shows interesting outcomes as have come out of this paper.
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A Founder server with 4G RAM and Intel Xeon E5504 CPU is used to conduct the experiments.
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Acknowledgments
This work is supported by the Fundamental Research Funds for the Central Universities (No. 09CX04031A). The authors are grateful for the anonymous reviewers who made constructive comments.
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Li, C., Shi, G. Weights optimization for multi-instance multi-label RBF neural networks using steepest descent method. Neural Comput & Applic 22, 1563–1569 (2013). https://doi.org/10.1007/s00521-012-0815-7
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DOI: https://doi.org/10.1007/s00521-012-0815-7