• DocumentCode
    514208
  • Title

    Online semi-supervised learning: Application to dynamic learning from RADAR data

  • Author

    Yver, B.

  • Author_Institution
    CEA, CESTA, Le Barp, France
  • fYear
    2009
  • fDate
    12-16 Oct. 2009
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Dynamic learning from RADAR data is a new challenge which needs the development of new classification methods using an online semi-supervised learning approach. Except a very recent paper presented at ECML 2008, no algorithm in the literature can deal with this problem. Based on a theoretical analysis of the limitations and advantages of several semi-supervised and online learning methods, we proposed four algorithms as potential solutions to the dynamic learning problem which will be tested in next monthes.
  • Keywords
    learning (artificial intelligence); radar computing; ECML; dynamic learning; online semisupervised learning; radar data; Algorithm design and analysis; Data mining; Databases; Learning systems; Radar applications; Radar measurements; Semisupervised learning; Supervised learning; Testing; Time measurement; classification; data flow; dynamic learning; online semi-supervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Radar Conference - Surveillance for a Safer World, 2009. RADAR. International
  • Conference_Location
    Bordeaux
  • Print_ISBN
    978-2-912328-55-7
  • Type

    conf

  • Filename
    5438391