• DocumentCode
    2202127
  • Title

    Active Set Fuzzy Support Vector ϵ-Insensitive Regression Approach

  • Author

    Singh, Rampal ; Balasundaram, S.

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Delhi, New Delhi, India
  • fYear
    2008
  • fDate
    20-22 Dec. 2008
  • Firstpage
    879
  • Lastpage
    883
  • Abstract
    In this paper a new fuzzy linear support vector machine formulation for regression problems is proposed and solved by the active set computational strategy. In this model, to each input data a fuzzy membership value is associated so that the input data can contribute proportionally to the learning of the decision surface. The proposed method has the advantage that its solution is obtained by solving a system of linear equations at a finite number of times rather than solving a quadratic optimization problem. Numerical experiments have been performed and the results obtained are in close agreement with the exact solution of the problems considered which clearly shows the effectiveness of the method.
  • Keywords
    fuzzy set theory; learning (artificial intelligence); minimisation; quadratic programming; regression analysis; support vector machines; active set fuzzy support vector ϵ-insensitive regression approach; decision surface learning; fuzzy linear support vector machine formulation; fuzzy membership value; linear equation; quadratic optimization problem; unconstrained minimization problem; Computer science; Equations; Fuzzy set theory; Fuzzy sets; Learning systems; Optimization methods; Quadratic programming; Statistical learning; Support vector machine classification; Support vector machines; Active Set; Fuzzy Support Vector Machines; Regression;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Computer Theory and Engineering, 2008. ICACTE '08. International Conference on
  • Conference_Location
    Phuket
  • Print_ISBN
    978-0-7695-3489-3
  • Type

    conf

  • DOI
    10.1109/ICACTE.2008.153
  • Filename
    4737083