Abstract
The MIMLSVM algorithm is to transform the MIML learning problem into a single-instance multi-label learning problem, which is used as a bridge to degenerate into a single-instance single-label learning. However, this degradation algorithm is relatively easy to understand, but in the degradation process will lose some information, affecting the classification effect. By using multi-tasking learning, E-MIMLSVM+ is used to combine tag relevance to improve the algorithm MIMLSVM+. In order to make full use of the unlabeled samples to improve the classification accuracy, the paper improves MIMLSVM algorithm by using the semi-supervised learning method. Experimental results show that the proposed method can achieve higher classification accuracy.
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Acknowledgment
The work was sponsored by the Institute of computer vision, image processing and pattern recognition, Zhejiang Sci-Tech University.
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Huang, W., You, H., Mei, L., Chen, Y., Huang, M. (2018). Improvement of E-MIMLSVM+ Algorithm Based on Semi-Supervised Learning. In: Tavana, M., Patnaik, S. (eds) Recent Developments in Data Science and Business Analytics. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-72745-5_48
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DOI: https://doi.org/10.1007/978-3-319-72745-5_48
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