MinOver Revisited for Incremental Support-Vector-Classification

  • Thomas Martinetz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3175)

Abstract

The well-known and very simple MinOver algorithm is reformulated for incremental support vector classification with and without kernels. A modified proof for its \(\mathcal{O}(t^{1/2})\) convergence is presented, with t as the number of training steps. Based on this modified proof it is shown that even a convergence of at least \(\mathcal{O}(t^{1})\) is given. This new convergence bound for MinOver is confirmed by computer experiments on artificial data sets. The computational effort per training step scales as \(\mathcal{O}(N)\) with the number N of training patterns.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Thomas Martinetz
    • 1
  1. 1.Institute for Neuro- and BioinformaticsUniversity of LübeckLübeckGermany

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