Asymptotic convergence of an SMO algorithm without any assumptions

  • Authors:
  • Chih-Jen Lin

  • Affiliations:
  • Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei

  • Venue:
  • IEEE Transactions on Neural Networks
  • Year:
  • 2002

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Abstract

The asymptotic convergence of C.-J. Lin (2001) can be applied to a modified SMO (sequential minimal optimization) algorithm by S.S. Keerthi et al. (2001) with some assumptions. The author shows that for this algorithm those assumptions are not necessary