Learning Support Vector Regression Models for Fast Radiation Dose Rate (CROSBI ID 41739)
Prilog u knjizi | izvorni znanstveni rad
Podaci o odgovornosti
Trontl, Krešimir ; Šmuc, Tomislav ; Pevec, Dubravko
engleski
Learning Support Vector Regression Models for Fast Radiation Dose Rate
In this chapter we consider the application of Support Vector Regression (SVR) in the field of radiation dose rate calculations, namely determination of gamma ray dose buildup factors. We demonstrate that SVR model for buildup factor determination can be applied as a fast engineering tool, replacing more traditional approaches based on semi-empirical formulas. More important is the fact that using general regression model like SVR in conjunction with machine learning methodology for the development and evaluation of learned models, provides a general approach for replacing complex simulation models. Therefore, we attempt to summarize research activities in a set of guidelines and procedures for performing an optimized search for the SVR model, for similar types of physical problems.
gamma dose rate, multi-layer shields, support vector regression, buildup factor
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Podaci o prilogu
427-462.
objavljeno
Podaci o knjizi
Machine Learning Research Progress
Peters, Hannah ; Vogel, Mia
New York (NY): Nova Science Publishers
2010.
978-1-60456-646-8