On the convergence of a modified version of the SVMlight algorithm (Articolo in rivista)

Type
Label
  • On the convergence of a modified version of the SVMlight algorithm (Articolo in rivista) (literal)
Anno
  • 2005-01-01T00:00:00+01:00 (literal)
Alternative label
  • Palagi, L.; Sciandrone, M. (2005)
    On the convergence of a modified version of the SVMlight algorithm
    in Optimization methods & software (Print)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Palagi, L.; Sciandrone, M. (literal)
Pagina inizio
  • 315 (literal)
Pagina fine
  • 332 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 20 (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Sapienza Università di Roma, Università di Firenze (literal)
Titolo
  • On the convergence of a modified version of the SVMlight algorithm (literal)
Abstract
  • In thiswork, we consider the convex quadratic programming problem arising in support vector machine (SVM), which is a technique designed to solve a variety of learning and pattern recognition problems. Since the Hessian matrix is dense and real applications lead to large-scale problems, several decomposition methods have been proposed, which split the original problem into a sequence of smaller subproblems.SVMlight algorithm is a commonly used decomposition method for SVM, and its convergence has been proved only recently under a suitable block-wise convexity assumption on the objective function. In SVMlight algorithm, the size q of the working set, i.e. the dimension of the subproblem, can be any even number. In the present paper, we propose a decomposition method on the basis of a proximal point modification of the subproblem and the basis of a working set selection rule that includes, as a particular case, the one used by the SVMlight algorithm. We establish the asymptotic convergence of the method, for any size q >= 2 of the working set, and without requiring any further block-wise convexity assumption on the objective function. Furthermore, we show that the algorithm satisfies in a finite number of iterations a stopping criterion based on the violation of the optimality conditions. (literal)
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