Pattern Recognition and Image Analysis

, Volume 28, Issue 1, pp 163–167 | Cite as

Total Margin Based Balanced Relative Margin Machine

  • Yankun Wu
  • Huimin Pei
  • Ping Zhong
Applied Problems


Inspired by the total margin algorithm, we extend balanced relative margin machine (BRMM) by introducing surplus variables, and propose a total margin based balanced relative (TM-BRMM). TMBRMM not only solves the loss of information points involved, but also addresses outliers at the outer boundaries that limit the maximum distance from points to separating hyperplane. Furthermore, by means of kernel function, it is easy to solve nonlinear separable datasets. The experiments on UCI datasets verify the feasibility and superiority of TM-BRMM.


support vector machine total margin relative margin kernel method 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    C. Cortes and V. Vapnik, “Support vector networks,” Mach. Learn. 20 (3), 273–297 (1995).MATHGoogle Scholar
  2. 2.
    T. S. Furey, N. Cristianini, N. Duffy, et al., “Support vector machine classification and validation of cancer tissue samples using microarray expression data,” Bioinformatics 16 (10), 906–914 (2000).CrossRefGoogle Scholar
  3. 3.
    X. F. Ling, J. Yang, and Y. E. Chen-Zhou, “Support vector machine-based human face recognition method,” Infrared Laser Eng. 5 (318–322), 327 (2001).Google Scholar
  4. 4.
    E. Osuna, R. Freund, and F. Girosi, “Training support vector machines: an application to face detection,” in Proc. CVPR (Hilton Head Island, SC, 2000), pp. 130–136.Google Scholar
  5. 5.
    H. C. Liu, M. A. Shu-Yuan, W. U. Ping-Dong, et al., “Handwritten digits recognition for automatic analysis system of UK psychology test,” J. Beijing Inst. Technol. 22 (5), 599–603 (1999).Google Scholar
  6. 6.
    L. M. Zeng and W. U. Xiang-Bin, “Research on SVM and its application of remote sense image classification for regions of interest,” Comput. Eng. Appl. 45 (6), 243–245 (2006).Google Scholar
  7. 7.
    Q. Chen, G. N. Cao, and L. Chen, “Application of support vector machine to atmospheric pollution prediction,” Comput. Technol. Develop. 32 (12), 61–65 (2010).Google Scholar
  8. 8.
    Y. Min, Y. Yun, and H. Nakayama, “A role of total margin in support vector machines,” in Proc. Int. Joint Conf. on Neural Networks (Portland, OR, 2003), Vol. 3, pp. 2049–2053.Google Scholar
  9. 9.
    H. L. Dai, “Class imbalance learning via a fuzzy total margin based support vector machine,” Appl. Soft Comput. C 31, 172–184 (2015).CrossRefGoogle Scholar
  10. 10.
    Y. H. Liu and Y. T. Chen, “Face recognition using total margin-based adaptive fuzzy support vector machines,” IEEE Trans Neural Networks 18 (1), 178–192 (2007).CrossRefGoogle Scholar
  11. 11.
    H. Pei, Y. Chen, Y. Wu, and P. Zhong, “Laplacian total margin support vector machine based on within-class scatter,” Knowledge-Based Syst. 119, 152–165 (2017).CrossRefGoogle Scholar
  12. 12.
    P. K. Shivaswamy and T. Jebara, “Relative margin machines,” in Proc. Conf. on Neural Information Processing Systems (Vancouver, Dec. 2008), pp. 1481–1488.Google Scholar
  13. 13.
    Z. Y. Long, J. H. Liu, and L. U. Han yu, “Research of fuzzy ?-relative margin machine based on total margin,” Microelectron. Comput. 6, 167–171 (2012).Google Scholar
  14. 14.
    I. Kotsia and I. Patras, “Relative margin support tensor machines for gait and action recognition,” in Proc. ACM Int. Conf. on Image and Video Retrieval, CIVR (Xi’an, July 2010), pp. 446–453.CrossRefGoogle Scholar
  15. 15.
    Y. Song, W. Zhu, Y. Xiao, and P. Zhong, “Robust relative margin support vector machines,” J. Algorithms Comput. Technol. 11 (2), 186–191 (2017).MathSciNetCrossRefGoogle Scholar
  16. 16.
    V. Eidelman, Y. Marton, and P. Resnik, “Online relative margin maximization for statistical machine translation,” in Proc. Meeting of the Association for Computational Linguistics (Sofia, 2013), pp. 1116–1126.Google Scholar
  17. 17.
    A. B. Ashraf, S. Lucey, and T. Chen, “Reinterpreting the application of Gabor filters as a manipulation of the margin in linear support vector machines,” IEEE Trans. Software Eng. 32 (7), 1335–1341 (2010).Google Scholar
  18. 18.
    M. M. Krell, D. Feess, and S. Straube, “Balanced relative margin machine–the missing piece between FDA and SVM classification,” Pattern Recogn. Lett. 41 (1), 43–52 (2014).CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.College of ScienceChina Agricultural UniversityBeijingChina

Personalised recommendations