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Robust Parametric Twin Support Vector Machine for Pattern Classification

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Abstract

In this paper, we propose a robust parametric twin support vector machine (RPTWSVM) classifier based on Parametric-\(\nu \)-Support Vector Machine (Par-\(\nu \)-SVM) and twin support vector machine. In order to capture heteroscedastic noise present in the training data, RPTWSVM finds a pair of parametric margin hyperplanes that automatically adjusts the parametric insensitive margin to incorporate the structural information of data. The proposed model of RPTWSVM is not only useful in controlling the heteroscedastic noise but also has much faster training speed when compared to Par-\(\nu \)-SVM. Experimental results on several machine learning benchmark datasets show the advantages of RPTWSVM both in terms of generalization ability and training speed over other related models.

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Acknowledgements

The authors would like to thank the editor and the anonymous reviewers whose valuable comments and feedback have helped us to improve the content and presentation of the paper.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Mathematics and Computer Science, South Asian University, New Delhi, India

    Reshma Rastogi & Sweta Sharma

  2. Department of Mathematics, Indian Institute of Technology, Delhi, New Delhi, India

    Suresh Chandra

Authors
  1. Reshma Rastogi
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  2. Sweta Sharma
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  3. Suresh Chandra
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Correspondence to Reshma Rastogi.

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Rastogi, R., Sharma, S. & Chandra, S. Robust Parametric Twin Support Vector Machine for Pattern Classification. Neural Process Lett 47, 293–323 (2018). https://doi.org/10.1007/s11063-017-9633-3

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  • DOI: https://doi.org/10.1007/s11063-017-9633-3

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