Rigorous proof of termination of SMO algorithm for support vector machines

Norikazu Takahashi, Tetsuo Nishi

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

Sequential minimal optimization (SMO) algorithm is one of the simplest decomposition methods for learning of support vector machines (SVMs). Keerthi and Gilbert have recently studied the convergence property of SMO algorithm and given a proof that SMO algorithm always stops within a finite number of iterations. In this letter, we point out the incompleteness of their proof and give a more rigorous proof.

Original languageEnglish
Pages (from-to)774-776
Number of pages3
JournalIEEE Transactions on Neural Networks
Volume16
Issue number3
DOIs
Publication statusPublished - May 2005
Externally publishedYes

Fingerprint

Termination
Support vector machines
Support Vector Machine
Optimization Algorithm
Incompleteness
Decomposition Method
Convergence Properties
Decomposition
Iteration
Learning

Keywords

  • Convergence
  • Sequential minimal optimization (SMO) algorithm
  • Support vector machines (SVMs)
  • Termination

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Hardware and Architecture

Cite this

Rigorous proof of termination of SMO algorithm for support vector machines. / Takahashi, Norikazu; Nishi, Tetsuo.

In: IEEE Transactions on Neural Networks, Vol. 16, No. 3, 05.2005, p. 774-776.

Research output: Contribution to journalArticle

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