• DocumentCode
    3573446
  • Title

    Sample Selection Based on Minimum Likelihood Ratio

  • Author

    Liu, Gang ; Cui, Yu-jing ; Zhang, Hong-Gang ; Guo, Jun

  • Author_Institution
    Beijing Univ. of Posts & Telecommun., Beijing
  • Volume
    1
  • fYear
    2007
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Training data have important effect on recognition system performance. This paper proposes an algorithm of sample selection based on minimum likelihood ratio (MLR) which obtaining boundary samples for training. The experiment results show that this method is effective in improving performance of the recognition system.
  • Keywords
    boundary-value problems; data analysis; pattern recognition; sampling methods; boundary samples; minimum likelihood ratio; pattern recognition system; training data; Clustering methods; Cybernetics; Data engineering; Data mining; Machine learning; Multi-layer neural network; Neural networks; Pattern recognition; System performance; Training data; Boundary samples; Likelihood ratio; MLR; Samples selection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2007 International Conference on
  • Print_ISBN
    978-1-4244-0973-0
  • Electronic_ISBN
    978-1-4244-0973-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2007.4370105
  • Filename
    4370105
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3573446