Self-organizing neural networks using discontinuous teacher data for incremental category learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we develop a neural model that forms categories of inputs for some practical applications such as pattern recognition, learning, image processing, and trend analysis. The developed model is based on natural mechanisms of biological behavior instead of artificial one such as clustering algorithms. The essential point of the model is to regard the teacher information as a first priority for an accurate learning. Then, the model can carry the accurate classification of complex and imbalanced categories by using discontinuous teacher data under an incremental learning environment. Simulation results demonstrate the usefulness and the weakness of the model on practical category formation tasks.

Original languageEnglish
Title of host publication2006 SICE-ICASE International Joint Conference
Number of pages5
Publication statusPublished - 2006
Event2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of
Duration: 2006 Oct 182006 Oct 21

Publication series

Name2006 SICE-ICASE International Joint Conference


Other2006 SICE-ICASE International Joint Conference
Country/TerritoryKorea, Republic of


  • Category formation
  • Clustering
  • Incremental learning
  • Neural networks
  • Pattern recognition
  • Principal component analysis
  • Self-organization

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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