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Nested Sequential Minimal Optimization for Support Vector Machines

  • Alessandro Ghio
  • Davide Anguita
  • Luca Oneto
  • Sandro Ridella
  • Carlotta Schatten
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)

Abstract

We propose in this work a nested version of the well–known Sequential Minimal Optimization (SMO) algorithm, able to contemplate working sets of larger cardinality for solving Support Vector Machine (SVM) learning problems. Contrary to several other proposals in literature, neither new procedures nor numerical QP optimizations must be implemented, since our proposal exploits the conventional SMO method in its core. Preliminary tests on benchmarking datasets allow to demonstrate the effectiveness of the presented method.

Keywords

Support Vector Machine Convex Constrained Quadratic Programming Sequential Minimal Optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alessandro Ghio
    • 1
  • Davide Anguita
    • 1
  • Luca Oneto
    • 1
  • Sandro Ridella
    • 1
  • Carlotta Schatten
    • 1
  1. 1.DITENUniversity of GenovaGenovaItaly

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