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Lagrangian Twin-Bounded Support Vector Machine Based on L2-Norm

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Recent Developments in Machine Learning and Data Analytics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 740))

Abstract

In this paper, a new convergent, flexible and easy technique is proposed for twin-bounded support vector machine (TBSVM) with the dual formulation. Here, a strongly convex objective function is constructed for proposed Lagrangian twin bounded support vector machine (LTBSVM) in consideration with L2-norm of the vector of slack variables in place of L1-norm vector of the slack variable. Twin support vector machine (TSVM) and TBSVM are used to solve quadratic programming problem (QPP) but in this proposed scheme, we consider an iterative method to solve a pair of linearly convergent equations. Further, a comparative analysis of generalized performance for TSVM, TBSVM, and proposed LTBSVM are tabulated for real world and as well as for synthetic datasets in consideration both the cases linear and nonlinear. This comparative analysis signifies the genuine improvement for the proposed method in terms of generalization performance and learning speed.

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Authors and Affiliations

  1. Computer Science and Engineering, National Institute of Technology, Yupia, Arunachal Pradesh, India

    Umesh Gupta & Deepak Gupta

Authors
  1. Umesh Gupta
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  2. Deepak Gupta
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Corresponding author

Correspondence to Deepak Gupta .

Editor information

Editors and Affiliations

  1. College of Engineering and Applied Science, University of Colorado Colorado Springs, Colorado Springs, CO, USA

    Jugal Kalita

  2. Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Department of Computer Applications, Sikkim Manipal University, Sikkim, India

    Samarjeet Borah

  4. Department of Computer Applications, Sikkim Manipal University, Sikkim, India

    Ratika Pradhan

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Gupta, U., Gupta, D. (2019). Lagrangian Twin-Bounded Support Vector Machine Based on L2-Norm. In: Kalita, J., Balas, V., Borah, S., Pradhan, R. (eds) Recent Developments in Machine Learning and Data Analytics. Advances in Intelligent Systems and Computing, vol 740. Springer, Singapore. https://doi.org/10.1007/978-981-13-1280-9_40

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