Data preprocessing for signal separation in high energy physics experiments

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F18%3A00325059" target="_blank" >RIV/68407700:21340/18:00325059 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Data preprocessing for signal separation in high energy physics experiments
Original language description
Whilst classifying high energy physics data, it is often necessary to deal with the problem of dataset high dimensions. Dimensionality reduction techniques represent usual way of reducing the number of physical variables while preserving the data structure and the major part of information carried. This enables faster classification algorithms and produces more accurate results. Particularly, influence of the principal component analysis on the quality and speed of the classification by a supervised divergence decision tree is discussed. In the process, the Rényi divergence, kernel density estimator, and optimization numerical techniques, are applied.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
SPMS 2018 - Stochastic and Physical Monitoring Systems, Proceedings of the international conference
ISBN
978-80-01-06501-3
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
43-48
Publisher name
Česká technika - nakladatelství ČVUT
Place of publication
Praha
Event location
Dobřichovice
Event date
Jun 18, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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