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International Conference on Learning and Intelligent Optimization

LION 2014: Learning and Intelligent Optimization pp 291–305Cite as

Robust Support Vector Machines with Polyhedral Uncertainty of the Input Data

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8426))

Abstract

In this paper, we use robust optimization models to formulate the support vector machines (SVMs) with polyhedral uncertainties of the input data points. The formulations in our models are nonlinear and we use Lagrange multipliers to give the first-order optimality conditions and reformulation methods to solve these problems. In addition, we have proposed the models for transductive SVMs with input uncertainties.

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Author information

Authors and Affiliations

  1. Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ, 85721, USA

    Neng Fan & Elham Sadeghi

  2. Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, 32611, USA

    Panos M. Pardalos

Authors
  1. Neng Fan
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  2. Elham Sadeghi
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  3. Panos M. Pardalos
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Corresponding author

Correspondence to Neng Fan .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  2. AT&T Labs Research, Middletown, New Jersey, USA

    Mauricio G.C. Resende

  3. University of Florida, Gainesville, Florida, USA

    Chrysafis Vogiatzis

  4. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Jose L. Walteros

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© 2014 Springer International Publishing Switzerland

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Fan, N., Sadeghi, E., Pardalos, P.M. (2014). Robust Support Vector Machines with Polyhedral Uncertainty of the Input Data. In: Pardalos, P., Resende, M., Vogiatzis, C., Walteros, J. (eds) Learning and Intelligent Optimization. LION 2014. Lecture Notes in Computer Science(), vol 8426. Springer, Cham. https://doi.org/10.1007/978-3-319-09584-4_26

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  • DOI: https://doi.org/10.1007/978-3-319-09584-4_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09583-7

  • Online ISBN: 978-3-319-09584-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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