Face recognition by concept formation neural structure

Yosuke Koyanaka, Noriyasu Homma, Masao Sakai, Kenichi Abe

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

1 Citation (Scopus)


In this paper, we develop a new neural model that deals with continuation value of inputs for some practical applications of pattern recognition task. An essential core of the model is use of a novel vector representation of a target concept in a multi-level informational hierarchy that makes the model possess category formation ability from incomplete observation of the target. Simulation results demonstrate the usefulness of the model for a facial image recognition task, even if it is carried out under an incremental and unsupervised learning environment.

Original languageEnglish
Title of host publicationProceedings of the SICE Annual Conference
Number of pages5
Publication statusPublished - 2004
EventSICE Annual Conference 2004 - Sapporo, Japan
Duration: 2004 Aug 42004 Aug 6


OtherSICE Annual Conference 2004


  • Concept formation
  • Hebbian rule
  • Incremental learning
  • Neural networks
  • Pattern recognition
  • Self-organization

ASJC Scopus subject areas

  • Engineering(all)


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