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Novel approaches of data-mining in experimental physics

Identifieur interne : 000694 ( Main/Exploration ); précédent : 000693; suivant : 000695

Novel approaches of data-mining in experimental physics

Auteurs : Gennady A. Ososkov [Russie]

Source :

RBID : ISTEX:1C7E04764AEDA2A5C0A1B862F5B7B7A28A0D1079

Abstract

Data mining for processing experimental data in high energy and nuclear physics led to many multiparametric problems, two of them are considered: (i) hypothesis testing and classification approaches based on artificial neural networks and boosted decision trees (ii) clustering of large amounts of data by so-called growing neural gas. Some examples from the practice of the Joint Institute for Nuclear Research are given to show how to prepare data to deal with those approaches.


Url:
DOI: 10.2478/v10127-012-0013-0


Affiliations:


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