Title :
Statistical learning methods in high-energy- and astrophysics
Author :
Kiesling, Christian ; Zimmermann, Jens
Author_Institution :
Max-Planck-Inst. fur Phys., Munchen, Germany
Abstract :
We discuss several popular statistical learning methods used in high-energy physics and astrophysics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astrophysics. The statistical learning methods are compared with each other and with standard methods for the respective application.
Keywords :
astronomy computing; decision trees; learning (artificial intelligence); statistical analysis; support vector machines; astrophysics; high-energy physics; particle physics; statistical learning methods; Area measurement; Astrophysics; Data mining; Extraterrestrial measurements; Learning systems; Mathematical model; Parameter estimation; Statistical learning; Statistics; Testing;
Conference_Titel :
Intelligent Sensors, Sensor Networks and Information Processing Conference, 2004. Proceedings of the 2004
Print_ISBN :
0-7803-8894-1
DOI :
10.1109/ISSNIP.2004.1417483
