Kernel and divergence techniques in high energy physics separations

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F17%3A00314557" target="_blank" >RIV/68407700:21340/17:00314557 - isvavai.cz</a>
Result on the web
<a href="http://iopscience.iop.org/article/10.1088/1742-6596/898/7/072004" target="_blank" >http://iopscience.iop.org/article/10.1088/1742-6596/898/7/072004</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1088/1742-6596/898/7/072004" target="_blank" >10.1088/1742-6596/898/7/072004</a>

Result language
angličtina
Original language name
Kernel and divergence techniques in high energy physics separations
Original language description
Binary decision trees under the Bayesian decision technique are used for supervised classifcation of high-dimensional data. We present a great potential of adaptive kernel density estimation as the nested separation method of the supervised binary divergence decision tree. Also, we provide a proof of alternative computing approach for kernel estimates utilizing Fourier transform. Further, we apply our method to Monte Carlo data set from the particle accelerator Tevatron at D0 experiment in Fermilab and provide final top-antitop signal separation results. We have achieved up to 82% AUC while using the restricted feature selection entering the signal separation procedure.
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
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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