1. ELARTU — Інституційний репозитарій ТНТУ імені Івана Пулюя
  2. FOSS/ВВПЗ
  3. FOSS Lviv
  4. FOSS Lviv 2017
Please use this identifier to cite or link to this item: http://elartu.tntu.edu.ua/handle/lib/24469

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dc.contributor.authorПархац, К. Г.
dc.coverage.temporal27-30 квітня 2017 року
dc.coverage.temporal27-30 April 2017
dc.date.accessioned2018-04-01T21:09:23Z-
dc.date.available2018-04-01T21:09:23Z-
dc.date.created2017-04-27
dc.date.issued2017-04-27
dc.identifier.citationПархац К. Г. Выкарыстанне бібліятэкі scikit-learn ў задачы класіфікацыі рухаў трэкбола / Пархац К. Г. // FOSS Lviv 2017, 27-30 квітня 2017 року. — Львів : Т.Б. Сорока, 2017. — С. 71–73.
dc.identifier.isbn978-966-2598-86-5
dc.identifier.urihttp://elartu.tntu.edu.ua/handle/lib/24469-
dc.description.abstractA scikit-learn machine learning library is discussed in conjunction with its usage in the task of a trackball-specific gesture recognition. Specific of the library and its place in the row of open source machine learning tools is covered. Details of the trackball movement recognition solved with use of the support vector machines approach are presented as far as keynotes for the chosen method.
dc.format.extent71-73
dc.language.isobe
dc.publisherТ.Б. Сорока
dc.relation.ispartofМатеріали сьомої науково-практичної конференції FOSS Lviv 2017, 2017
dc.relation.ispartofProceedings of Free/Libre and Open-Source Software Lviv-2017, 2017
dc.relation.urihttps://youtu.be/6Ec0AhQ5BaM
dc.relation.urihttp://www.infoworld.com/article/2853707/robotics/11-open-source-tools-machine-learning.html
dc.relation.urihttp://scikit-learn.org/stable/
dc.titleВыкарыстанне бібліятэкі scikit-learn ў задачы класіфікацыі рухаў трэкбола
dc.title.alternativeВикористання бібліотеки scikit-learn в завданні класифікації рухів трекбола
dc.title.alternativeUsing scikit-learn library classification problem of trackball movements
dc.typeConference Abstract
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.format.pages3
dc.relation.references1. Yegulalp S. 11 open source tools to make the most of machine learning // InfoWorld, Dec 4, 2014. http://www.infoworld.com/article/2853707/robotics/11-open-source-tools-machine-learning.html
dc.relation.references2. Scikit-learn: machine learning in Python. http://scikit-learn.org/stable/
dc.relation.references3. Pedregosa F. et al. Scikit-learn: machine learning in Python // Journal of Machine Learning Research, № 12, 2011. – P. 2825–2830.
dc.relation.referencesen1. Yegulalp S. 11 open source tools to make the most of machine learning, InfoWorld, Dec 4, 2014. http://www.infoworld.com/article/2853707/robotics/11-open-source-tools-machine-learning.html
dc.relation.referencesen2. Scikit-learn: machine learning in Python. http://scikit-learn.org/stable/
dc.relation.referencesen3. Pedregosa F. et al. Scikit-learn: machine learning in Python, Journal of Machine Learning Research, No 12, 2011, P. 2825–2830.
dc.identifier.citationenParkhats K. H. (2017) Vykorystannia biblioteky scikit-learn v zavdanni klasyfikatsii rukhiv trekbola. FOSS Lviv 2017 (Lviv, 27-30 April 2017), pp. 71-73 [in Belarusian].
dc.contributor.affiliationБрэсцкі дзяржаўны тэхнічны універсітэт, konstantinparhoc@gmail.com
dc.citation.journalTitleМатеріали сьомої науково-практичної конференції FOSS Lviv 2017
dc.citation.spage71
dc.citation.epage73
dc.citation.conferenceСьома науково-практична конференція FOSS Lviv 2017
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