Robust support vector machines with polyhedral uncertainty of the input data

Neng Fan, Elham Sadeghi, Panos M. Pardalos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationLearning and Intelligent Optimization - 8th International Conference, Lion 8, Revised Selected Papers
PublisherSpringer-Verlag
Pages291-305
Number of pages15
ISBN (Print)9783319095837
DOIs
StatePublished - Jan 1 2014
Event8th International Conference on Learning and Intelligent OptimizatioN, LION 2014 - Gainesville, FL, United States
Duration: Feb 16 2014Feb 21 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8426 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Learning and Intelligent OptimizatioN, LION 2014
CountryUnited States
CityGainesville, FL
Period2/16/142/21/14

Keywords

  • Classification
  • Nonlinear programming
  • Polyhedral uncertainty
  • Robust optimization
  • Support vector machines

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Fan, N., Sadeghi, E., & Pardalos, P. M. (2014). Robust support vector machines with polyhedral uncertainty of the input data. In Learning and Intelligent Optimization - 8th International Conference, Lion 8, Revised Selected Papers (pp. 291-305). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8426 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-09584-4_26