• DocumentCode
    1428553
  • Title

    Comparison of two different PNN training approaches for satellite cloud data classification

  • Author

    Tian, Bin ; Azimi-Sadjadi, Mahmood R.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA
  • Volume
    12
  • Issue
    1
  • fYear
    2001
  • fDate
    1/1/2001 12:00:00 AM
  • Firstpage
    164
  • Lastpage
    168
  • Abstract
    Presents a training algorithm for probabilistic neural networks (PNN) using the minimum classification error (MCE) criterion. A comparison is made between the MCE training scheme and the widely used maximum likelihood (ML) learning on a cloud classification problem using satellite imagery data
  • Keywords
    clouds; learning (artificial intelligence); maximum likelihood estimation; minimisation; neural nets; pattern classification; probability; remote sensing; MCE criterion; MCE training scheme; ML learning; PNN training approaches; maximum likelihood learning; minimum classification error criterion; probabilistic neural networks; satellite cloud data classification; satellite imagery data; Atmospheric modeling; Clouds; Maximum likelihood estimation; Neural networks; Neurons; Pattern recognition; Probability density function; Robustness; Satellites; Training data;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.896807
  • Filename
    896807
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1428553