Divergence Methods in Statistical Separations

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F14%3A00222891" target="_blank" >RIV/68407700:21340/14:00222891 - 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
Divergence Methods in Statistical Separations
Original language description
Binary decision tree is a popular tool for unsupervised classification of high-dimensional data. We propose an extension of binary decision tree in order to perform the supervised classification of data sets obtained from D0 experiment in Fermilab. A keyinsight we provide is the clustering driven only by a few physical variables achieving the maximal value of R´enyi divergence obtained from quantile histograms of signal and backgrounds. Further, we present the outcome of our initial signal separation under selected inner parameters.
Czech name
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Czech description
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Project
<a href="/en/project/LG12020" target="_blank" >LG12020: Advanced statistical analysis and non-statistical separation techniques for physical processing detection in data sets sampled by means of elementary particle accelerators.</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of Stochastic and Physical Monitoring Systems 2014
ISBN
978-80-01-05616-5
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
17-24
Publisher name
ČVUT v Praze
Place of publication
Praha
Event location
Malá Skála
Event date
Jun 23, 2014
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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