A learning method for robust support vector machines

Jun Guo, Norikazu Takahashi, Tetsuo Nishi

Research output: Contribution to journalArticlepeer-review


We propose an innovative learning algorithm for a support vector machine to be robust. As learning patterns it uses not only the prescribed learning patterns but also their neighbour patterns. The size of the proposed optimization problem to be solved is the same as the original one. Many simulations show the effectiveness of the proposed algorithm.

Original languageEnglish
Pages (from-to)474-479
Number of pages6
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - Dec 1 2004
Externally publishedYes

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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