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Improvement of E-MIMLSVM+ Algorithm Based on Semi-Supervised Learning

  • Wenqing Huang
  • Hui You
  • Li Mei
  • Yinlong Chen
  • Mingzhu Huang
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

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.

Keywords

SVM MIML Semi-supervised learning Multitask learning 

Notes

Acknowledgment

The work was sponsored by the Institute of computer vision, image processing and pattern recognition, Zhejiang Sci-Tech University.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Wenqing Huang
    • 1
  • Hui You
    • 1
  • Li Mei
    • 1
  • Yinlong Chen
    • 1
  • Mingzhu Huang
    • 1
  1. 1.School of Information, Zhejiang Sci-Tech University HangzhouHangzhou ShiChina

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