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Linear Classification of Data with Support Vector Machines and Generalized Support Vector Machines

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Engineering Mathematics II

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 179))

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Abstract

In this paper, we study the support vector machine and introduced the notion of generalized support vector machine for classification of data. We show that the problem of generalized support vector machine is equivalent to the problem of generalized variational inequality and establish various results for the existence of solutions. Moreover, we provide various examples to support our results.

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Acknowledgements

Talat Nazir and Xiaomin Qi are grateful to the Erasmus Mundus project FUSION for supporting the research visit to Mälardalen University, Sweden, and to the Research environment MAM in Mathematics and Applied Mathematics, Division of Applied Mathematics, the School of Education, Culture and Communication of Mälardalen University for creating excellent research environment.

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Authors and Affiliations

  1. Division of Applied Mathematics, School of Education, Culture and Communication, Mälardalen University, Box 883, 721 23, Västerås, Sweden

    Talat Nazir, Xiaomin Qi & Sergei Silvestrov

  2. Department of Mathematics, COMSATS Institute of Information Technology, Abbottabad, 22060, Pakistan

    Talat Nazir

Authors
  1. Talat Nazir
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  2. Xiaomin Qi
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  3. Sergei Silvestrov
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Corresponding author

Correspondence to Talat Nazir .

Editor information

Editors and Affiliations

  1. Division of Applied Mathematics, School of Education, Culture and Communication, Mälardalen University,, Västerås, Sweden

    Sergei Silvestrov

  2. Division of Applied Mathematics,, School of Education, Culture and Communication, Mälardalen University, Västerås, Sweden

    Milica Rančić

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Nazir, T., Qi, X., Silvestrov, S. (2016). Linear Classification of Data with Support Vector Machines and Generalized Support Vector Machines. In: Silvestrov, S., Rančić, M. (eds) Engineering Mathematics II. Springer Proceedings in Mathematics & Statistics, vol 179. Springer, Cham. https://doi.org/10.1007/978-3-319-42105-6_17

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