A novel competitive learning algorithm for the parametric classification with Gaussian distributions

Wen Jyi Hwang, Bo Yuan Ye, Chin Tsai Lin

Research output: Contribution to journalArticle

5 Citations (Scopus)


A competitive learning algorithm for the parametric classification of Gaussian sources is presented in this letter. The algorithm iteratively estimates the mean and prior probability of each class during the training. Bayes rule is then used for classification based on the estimated information. Simulation results show that the proposed algorithm outperforms k-means and LVQ algorithms for the parametric classification.

Original languageEnglish
Pages (from-to)375-380
Number of pages6
JournalPattern Recognition Letters
Issue number5
Publication statusPublished - 2000 Jan 1



  • Competitive learning
  • Maximum likelihood estimation
  • Neural networks
  • Parametric classification
  • Pattern recognition

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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