Original title: Statistical Expectation of High Energy Physics Data Sets Separation Algorithms
Authors: Hakl, František
Document type: Papers
Conference/Event: SPMS 2013, Nebřich (CZ), 2013-06-24 / 2013-06-29
Year: 2013
Language: eng
Abstract: Article focuses on the application of the basic results of the statistical learning theory known as Probabilistic Approximately Correct learning in the evaluation and post-processing of unique physical data obtained from the detectors of particle accelerators. The aim of this article is not direct separation of the measured data but evaluation of the appropriateness of separation methods used. The main principles and results of the PAC learning theory are briefly summarized, the main characteristics of selected multivariable data separation algorithms are studied from the VC-dimension point of view. Finally, based on actual data sets obtained from Tevatron D$\emptyset$ experiment, some practical hints for separation method selection and numerical computation are derived.
Keywords: Decision trees; HEP data separation; Neural networks; Probably Approximately Correct Learning; Refutability; VC-dimension
Project no.: LG12020 (CEP)
Funding provider: GA MŠk
Host item entry: Stochastic and Physical Monitoring Systems 2013, ISBN 978-80-01-05383-6

Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at the institute of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0227782

Permalink: http://www.nusl.cz/ntk/nusl-166223


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