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Robust Support Vector Machines with Polyhedral Uncertainty of the Input Data

  • Neng FanEmail author
  • Elham Sadeghi
  • Panos M. Pardalos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Support vector machines Robust optimization Polyhedral uncertainty Nonlinear programming Classification 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Systems and Industrial EngineeringUniversity of ArizonaTucsonUSA
  2. 2.Center for Applied Optimization, Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA

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