A learning method for robust support vector machines

Jun Guo, Norikazu Takahashi, Tetsuo Nishi

Research output: Contribution to journalArticle

Abstract

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)
Volume3173
Publication statusPublished - 2004
Externally publishedYes

Fingerprint

Learning algorithms
Support vector machines
Support Vector Machine
Learning
Learning Algorithm
Optimization Problem
Simulation

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

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