• DocumentCode
    3647271
  • Title

    How to infer the informational energy from small datasets

  • Author

    Angel Cataron;Răzvan Andonie

  • Author_Institution
    Transilvania University of Braş
  • fYear
    2012
  • fDate
    5/1/2012 12:00:00 AM
  • Firstpage
    1065
  • Lastpage
    1070
  • Abstract
    Motivated by the problems in machine learning, we introduce a novel non-parametric estimator of Onicescu´s informational energy. Our method is based on the k-th nearest neighbor distances between the n sample points, where k is a fixed positive integer. For some standard distributions, we investigate the performance of the estimator for small datasets.
  • Keywords
    "Training","Approximation methods","Estimation","Complexity theory","Entropy","Gaussian distribution","Random variables"
  • Publisher
    ieee
  • Conference_Titel
    Optimization of Electrical and Electronic Equipment (OPTIM), 2012 13th International Conference on
  • ISSN
    1842-0133
  • Print_ISBN
    978-1-4673-1650-7
  • Type

    conf

  • DOI
    10.1109/OPTIM.2012.6231921
  • Filename
    6231921