https://oldena.lpnu.ua/handle/ntb/45493| Title: | A(n) Assumption in machine learning |
| Authors: | Klyushin, Dmitry Lyashko, Sergey Zub, Stanislav |
| Affiliation: | Taras Shevchenko National University of Kyiv |
| Bibliographic description (Ukraine): | Klyushin 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). |
| Bibliographic description (International): | Klyushin 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). |
| Is part of: | Computational Linguistics and Intelligent Systems (2), 2019 |
| Journal/Collection: | Computational Linguistics and Intelligent Systems |
| Volume: | 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019 |
| Issue Date: | 18-Apr-2019 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Lviv |
| Keywords: | machine learning sample homogeneity confidence interval order statistics variational series |
| Number of pages: | 7 |
| Page range: | 32-38 |
| Start page: | 32 |
| End page: | 38 |
| Abstract: | The 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. |
| URI: | https://ena.lpnu.ua/handle/ntb/45493 |
| ISSN: | 2523-4013 |
| Copyright owner: | © 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. |
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| Content type: | Article |
| Appears in Collections: | Computational linguistics and intelligent systems. – 2019 р. |
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