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Fuzzy Proximal Support Vector Classification Via Generalized Eigenvalues

  • Jayadeva
  • Reshma Khemchandani
  • Suresh Chandra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

Abstract

In this paper, we propose a fuzzy extension to proximal support vector classification via generalized eigenvalues. Here, a fuzzy membership value is assigned to each pattern, and points are classified by assigning them to the nearest of two non parallel planes that are close to their respective classes. The algorithm is simple as the solution requires solving a generalized eigenvalue problem as compared to SVMs, where the classifier is obtained by solving a quadratic programming problem. The approach can be used to obtain an improved classification when one has an estimate of the fuzziness of samples in either class.

Keywords

Support vector machines fuzzy data classification machine learning generalized eigenvalue problem proximal classifier 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jayadeva
    • 1
  • Reshma Khemchandani
    • 2
  • Suresh Chandra
    • 2
  1. 1.Department of Electrical EngineeringIndian Institute of Technology Delhi, Hauz KhasNew DelhiIndia
  2. 2.Department of MathematicsIndian Institute of Technology Delhi, Hauz KhasNew DelhiIndia

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