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On Lagrangian Twin Parametric-Margin Support Vector Machine

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Smart and Innovative Trends in Next Generation Computing Technologies (NGCT 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 827))

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Abstract

A new simple and linearly convergent scheme is proposed in this paper for the dual formulation of twin parametric-margin support vector machine. Here, instead of considering the 1-norm error of slack variables, we have considered 2-norm of the vector of slack variables to make the objective functions strongly convex. Further, the proposed method solves a pair of linearly convergent iterative schemes instead of solving a pair of quadratic programming problems as in case of twin support vector machine and twin parametric-margin support vector machine. The proposed method considers in finding two parametric-margin hyperplanes that makes it less sensitive to heteroscedastic noise structure. Our experiments, performed on synthetic and real-world datasets, conclude that the proposed method has comparable generalization performance and improved learning speed in comparison to twin support vector machine, Lagrangian twin support vector machine and twin parametric-margin support vector machine.

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Authors and Affiliations

  1. Computer Science and Engineering, NIT, Yupia, Arunachal Pradesh, India

    Parashjyoti Borah & Deepak Gupta

Authors
  1. Parashjyoti Borah
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  2. Deepak Gupta
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Correspondence to Parashjyoti Borah .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Patna, Patna, Bihar, India

    Pushpak Bhattacharyya

  2. University of Petroleum and Energy Studies, Dehradun, India

    Hanumat G. Sastry

  3. University of Petroleum and Energy Studies, Dehradun, India

    Venkatadri Marriboyina

  4. University of Petroleum and Energy Studies, Dehradun, India

    Rashmi Sharma

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Borah, P., Gupta, D. (2018). On Lagrangian Twin Parametric-Margin Support Vector Machine. In: Bhattacharyya, P., Sastry, H., Marriboyina, V., Sharma, R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 827. Springer, Singapore. https://doi.org/10.1007/978-981-10-8657-1_36

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  • DOI: https://doi.org/10.1007/978-981-10-8657-1_36

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8656-4

  • Online ISBN: 978-981-10-8657-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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