Published November 21, 2004
| Version v1
Journal article
Statistical learning methods in high-energy and astrophysics analysis
Creators
- 1. Forschungszentrum Juelich GmbH, Zentrallabor fuer Elektronik, 52425 Juelich (Germany) and Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)
- 2. Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)
Description
We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application
Additional details
Identifiers
- DOI
- 10.1016/j.nima.2004.07.088;
- PII
- S0168-9002(04)01535-9;
Publishing Information
- Journal Title
- Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment
- Journal Volume
- 534
- Journal Issue
- 1-2
- Journal Page Range
- p. 204-210
- ISSN
- 0168-9002
- CODEN
- NIMAER
Conference
- Title
- 9. international workshop on advanced computing and analysis techniques in physics research
- Dates
- 1-5 Dec 2003
- Place
- Tsukuba (Japan)
INIS
- Country of Publication
- Netherlands
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 37028899
- Subject category
- S99: GENERAL AND MISCELLANEOUS;
- Resource subtype / Literary indicator
- Conference
- Descriptors DEI
- ALGORITHMS; ASTROPHYSICS; CLASSIFICATION; DECISION TREE ANALYSIS; HIGH ENERGY PHYSICS; LEARNING
- Descriptors DEC
- MATHEMATICAL LOGIC; PHYSICS
Optional Information
- Copyright
- Copyright (c) 2004 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.