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
Link To Document