Semisupervised learning from different information sources

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


This paper studies the use of a semisupervised learning algorithm from different information sources. We first offer a theoretical explanation as to why minimising the disagreement between individual models could lead to the performance improvement. Based on the observation, this paper proposes a semisupervised learning approach that attempts to minimise this disagreement by employing a co-updating method and making use of both labeled and unlabeled data. Three experiments to test the effectiveness of the approach are presented in this paper: (i) webpage classification from both content and hyperlinks; (ii) functional classification of gene using gene expression data and phylogenetic data and (iii) machine self-maintaining from both sensory and image data. The results show the effectiveness and efficiency of our approach and suggest its application potentials. Springer-Verlag London Ltd.

Original languageEnglish (US)
Pages (from-to)289-309
Number of pages21
JournalKnowledge and Information Systems
Issue number3
StatePublished - Mar 2005
Externally publishedYes


  • Decision tree
  • Minimise disagreement
  • Semisupervised
  • Support vector machines
  • Unlabelled data

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence


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