A learning model of the feature-detecting cells for unsupervised pattern classification

Wen June Wang, Donq Liang Lee

Research output: Contribution to journalArticlepeer-review


A modified neural network unsupervised learning scheme by the feature-detecting cell is proposed. We improve the performance in learning categories by adding a modulation system and a competing system to the conventional feature-detecting cell model. With the aid of the modulation system and the competing system, the cluster prototype which is closest to the input pattern will win the competitions and a winner dominated learning will be controlled by the properly assigned bias values. The proposed model has the following features: it guarantees the corresponding feature-detecting cell of each input pattern to be formed regardless of the initial weights and the duplication (of the feature-detecting cells formation for each sampled input pattern) can be reduced.

Original languageEnglish
Pages (from-to)929-935
Number of pages7
JournalPattern Recognition Letters
Issue number9
StatePublished - Sep 1994


  • Feature-detecting cell
  • Neural network
  • Pattern classification
  • Unsupervised learning


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