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Total Margin Based Balanced Relative Margin Machine

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Abstract

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.

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Authors and Affiliations

  1. College of Science, China Agricultural University, Beijing, 100083, China

    Yankun Wu, Huimin Pei & Ping Zhong

Authors
  1. Yankun Wu
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  2. Huimin Pei
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  3. Ping Zhong
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Corresponding author

Correspondence to Ping Zhong.

Additional information

The article is published in the original.

Yankun Wu was born in China, in 1998. She is majoring in Mathematics and Applied Mathematics in China Agricultural University, Beijing, China.

Huimin Pei was born in 1987 in China. She has received the B.S. degree from Linyi University in 2011, and the M.S. degree from Beijing University of Technology in 2014. Now she is a PhD. student in College of Science, China Agricultural University. Her research interests include machine learning and support vector machines.

Ping Zhong is a professor and PhD supervisor in College of Science, China Agricultural University. She has published many papers. Her research interests include machine learning and support vector machines.

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Wu, Y., Pei, H. & Zhong, P. Total Margin Based Balanced Relative Margin Machine. Pattern Recognit. Image Anal. 28, 163–167 (2018). https://doi.org/10.1134/S1054661818010194

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  • DOI: https://doi.org/10.1134/S1054661818010194

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