Supervised Divergence Decision Tree with Adaptive Kernel Density Estimation in High Energy Physics
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F16%3A00304789" target="_blank" >RIV/68407700:21340/16:00304789 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Supervised Divergence Decision Tree with Adaptive Kernel Density Estimation in High Energy Physics
Original language description
Binary decision trees under the Bayesian decision technique are used for supervised classification of high-dimensional data. We present the great potential of adaptive kernel density estimation as the nested separation method of the supervised binary divergence decision tree. Further, we apply our method to Monte Carlo data set from the particle accelerator Tevatron at the DØ experiment in Fermilab and provide final top-antitop signal separation results.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
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
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
SPMS 2016 - Stochastic and Physical Monitoring Systems, Proceedings of the international conference
ISBN
978-80-01-06040-7
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
1-10
Publisher name
Česká technika - nakladatelství ČVUT
Place of publication
Praha
Event location
Dobřichovice
Event date
Jun 20, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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