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A p-norm Discrimination Model for Two Linearly Inseparable Sets

  • Pavlo Krokhmal
  • Robert Murphey
  • Panos M. Pardalos
  • Zhaohan Yu
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 40)

Summary

We propose a new p-norm linear discrimination model that generalizes the model of Bennett and Mangasarian (Optim. Methods Softw. 1:23–34, 1992) and reduces to linear programming problems with p-order conic constraints. We demonstrate that the developed model possesses excellent methodological and computational properties (e.g., it does not allow for a null separating hyperplane when the sets are linearly separable, etc.). The presented approach for handling linear programming problems with p-order conic constraints relies on construction of polyhedral approximations for p-order cones. A case study on several popular data sets that illustrates the advantages of the developed model is conducted.

Keywords

Linear Programming Problem Discrimination Model Separation Model Conic Constraint Steiner Minimum Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Pavlo Krokhmal
    • 1
  • Robert Murphey
    • 2
  • Panos M. Pardalos
    • 3
  • Zhaohan Yu
    • 1
  1. 1.Department of Mechanical and Industrial EngineeringUniversity of IowaIowa CityUSA
  2. 2.Air Force Research LabEglin AFBUSA
  3. 3.Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA

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