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Training Support Vector Machines via SMO-Type Decomposition Methods

  • Pai-Hsuen Chen
  • Rong-En Fan
  • Chih-Jen Lin
Conference paper
  • 550 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3735)

Abstract

This article gives a comprehensive study on SMO-type (Sequential Minimal Optimization) decomposition methods for training support vector machines. We propose a general and flexible selection of the two-element working set. Main theoretical results include 1) a simple asymptotic convergence proof, 2) a useful explanation of the shrinking and caching techniques, and 3) the linear convergence of this method. This analysis applies to any SMO-type implementation whose selection falls into the proposed framework.

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Pai-Hsuen Chen
    • 1
  • Rong-En Fan
    • 1
  • Chih-Jen Lin
    • 1
  1. 1.Department of Computer ScienceNational Taiwan University 

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