• DocumentCode
    2027390
  • Title

    Active learning for support vector regression in radiation shielding design

  • Author

    Duckic, Paulina ; Trontl, Kresimir ; Matijevic, Mario

  • Author_Institution
    Dept. of Appl. Phys., Univ. of Zagreb, Zagreb, Croatia
  • fYear
    2015
  • fDate
    20-24 July 2015
  • Firstpage
    311
  • Lastpage
    317
  • Abstract
    Recently a novel approach based on support vector regression technique has been proposed and tested for the estimation of multi layer buildup factors for gamma ray shielding calculations, while for neutron shielding calculations some initial analyses have been conducted. During the development of the model a number of questions regarding possible application of active learning measures have been raised. In this paper general applicability of the active learning measures on the problem, in particular data transfer method used in the investigation, and testing of the active procedure are discussed.
  • Keywords
    electronic data interchange; learning (artificial intelligence); physics computing; radiation; regression analysis; shielding; support vector machines; active learning; data transfer method; gamma ray shielding calculations; multi layer buildup factors; neutron shielding calculations; radiation shielding design; support vector regression; Accuracy; Data models; Kernel; Neutrons; Photonics; Support vector machines; Training; active learning; data transfer method; gamma buildup factor; neutron buildup factor; point kernel method; support vector regression;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    High Performance Computing & Simulation (HPCS), 2015 International Conference on
  • Conference_Location
    Amsterdam
  • Print_ISBN
    978-1-4673-7812-3
  • Type

    conf

  • DOI
    10.1109/HPCSim.2015.7237055
  • Filename
    7237055