Self-organizing networks for extracting jet features
(1991) In Computer Physics Communications 67(2). p.193-209- Abstract
Self-organizing neural networks are briefly reviewed and compared with supervised learning algorithms like back-propagation. The power of self-organization networks is in their capability of displaying typical features in a transparent manner. This is successfully demonstrated with two applications from hadronic jet physics; hadronization model discrimination and separation of b, c and light quarks.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/99b6f3db-cf43-465d-b248-ce0db339d4d6
- author
- Lönnblad, Leif LU ; Peterson, Carsten LU ; Pi, Hong and Rögnvaldsson, Thorsteinn
- organization
- publishing date
- 1991-12
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Computer Physics Communications
- volume
- 67
- issue
- 2
- pages
- 17 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:0026391884
- ISSN
- 0010-4655
- DOI
- 10.1016/0010-4655(91)90016-E
- language
- English
- LU publication?
- yes
- id
- 99b6f3db-cf43-465d-b248-ce0db339d4d6
- date added to LUP
- 2019-05-15 08:02:56
- date last changed
- 2024-01-01 04:42:30
@article{99b6f3db-cf43-465d-b248-ce0db339d4d6, abstract = {{<p>Self-organizing neural networks are briefly reviewed and compared with supervised learning algorithms like back-propagation. The power of self-organization networks is in their capability of displaying typical features in a transparent manner. This is successfully demonstrated with two applications from hadronic jet physics; hadronization model discrimination and separation of b, c and light quarks.</p>}}, author = {{Lönnblad, Leif and Peterson, Carsten and Pi, Hong and Rögnvaldsson, Thorsteinn}}, issn = {{0010-4655}}, language = {{eng}}, number = {{2}}, pages = {{193--209}}, publisher = {{Elsevier}}, series = {{Computer Physics Communications}}, title = {{Self-organizing networks for extracting jet features}}, url = {{http://dx.doi.org/10.1016/0010-4655(91)90016-E}}, doi = {{10.1016/0010-4655(91)90016-E}}, volume = {{67}}, year = {{1991}}, }