A(n) Assumption in machine learning

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dc.citation.epage38
dc.citation.journalTitleComputational Linguistics and Intelligent Systems
dc.citation.spage32
dc.citation.volume2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019
dc.contributor.affiliationTaras Shevchenko National University of Kyiv
dc.contributor.authorKlyushin, Dmitry
dc.contributor.authorLyashko, Sergey
dc.contributor.authorZub, Stanislav
dc.coverage.placenameLviv
dc.date.accessioned2019-10-31T13:21:05Z
dc.date.available2019-10-31T13:21:05Z
dc.date.created2019-04-18
dc.date.issued2019-04-18
dc.description.abstractThe commonly used statistical tools in machine learning are two-sample tests for verifying hypotheses on homogeneity, for example, for estimation of corpushomogeneity, testing text authorship and so on. Often, they are effective only for sufficiently large sample (n> 100) and have limited application in situations where the size of samples is small (n < 30). To solve the problem for small samples, methods of reproducing samples are often used: jackknife and bootstrap. We propose and investigate a family of homogeneity measures based on A(n) assumption that are effective both for small and large samples.
dc.format.extent32-38
dc.format.pages7
dc.identifier.citationKlyushin D. A(n) Assumption in machine learning / Dmitry Klyushin, Sergey Lyashko, Stanislav Zub // Computational Linguistics and Intelligent Systems. — Lviv : Lviv Politechnic Publishing House, 2019. — Vol 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019. — P. 32–38. — (Paper presentations).
dc.identifier.citationenKlyushin D. A(n) Assumption in machine learning / Dmitry Klyushin, Sergey Lyashko, Stanislav Zub // Computational Linguistics and Intelligent Systems. — Lviv Politechnic Publishing House, 2019. — Vol 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019. — P. 32–38. — (Paper presentations).
dc.identifier.issn2523-4013
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/45493
dc.language.isoen
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofComputational Linguistics and Intelligent Systems (2), 2019
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dc.rights.holder© 2019 for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors.
dc.subjectmachine learning
dc.subjectsample homogeneity
dc.subjectconfidence interval
dc.subjectorder statistics
dc.subjectvariational series
dc.titleA(n) Assumption in machine learning
dc.typeArticle

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