Nalazite se na CroRIS probnoj okolini. Ovdje evidentirani podaci neće biti pohranjeni u Informacijskom sustavu znanosti RH. Ako je ovo greška, CroRIS produkcijskoj okolini moguće je pristupi putem poveznice www.croris.hr
izvor podataka: crosbi !

Learning Support Vector Regression Models for Fast Radiation Dose Rate (CROSBI ID 41739)

Prilog u knjizi | izvorni znanstveni rad

Trontl, Krešimir ; Šmuc, Tomislav ; Pevec, Dubravko Learning Support Vector Regression Models for Fast Radiation Dose Rate // Machine Learning Research Progress / Peters, Hannah ; Vogel, Mia (ur.). New York (NY): Nova Science Publishers, 2010. str. 427-462

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

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

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

Povezanost rada

Elektrotehnika