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A Two-Norm Squared Fuzzy-Based Least Squares Twin Parametric-Margin Support Vector Machine

  • Parashjyoti Borah
  • Deepak Gupta
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

Abstract

A two-norm squared fuzzy-based least squares version of twin parametric-margin support vector machine is proposed to reduce the effect of outliers and noise by assigning fuzzy membership values to each training data samples. Further, by considering two-norm squared the slack variable multiplied to the fuzzy membership values makes the objective function strongly convex. Here, we substitute the inequality constraints of the primal problems with equality constraints to solve the two primal problems instead of solving two quadratic programming problems which eliminates the need of external optimization toolbox and provides with a lower computational cost. A performance comparison of the proposed method with twin support vector machine, least squares twin support vector machine, twin parametric-margin support vector machine and least squares twin parametric-margin support vector machine is presented in this paper.

Keywords

Support vector machine Fuzzy Quadratic programming problem Least squares Parametric-margin 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Computer Science & EngineeringNIT Arunachal PradeshYupiaIndia

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