Utilize este identificador para referenciar este registo: http://elartu.tntu.edu.ua/handle/lib/24469

Título: Выкарыстанне бібліятэкі scikit-learn ў задачы класіфікацыі рухаў трэкбола
Outros títulos: Використання бібліотеки scikit-learn в завданні класифікації рухів трекбола
Using scikit-learn library classification problem of trackball movements
Autor: Пархац, К. Г.
Affiliation: Брэсцкі дзяржаўны тэхнічны універсітэт, konstantinparhoc@gmail.com
Bibliographic description (Ukraine): Пархац 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.
Bibliographic description (International): Parkhats 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].
Is part of: Матеріали сьомої науково-практичної конференції FOSS Lviv 2017, 2017
Proceedings of Free/Libre and Open-Source Software Lviv-2017, 2017
Conference/Event: Сьома науково-практична конференція FOSS Lviv 2017
Journal/Collection: Матеріали сьомої науково-практичної конференції FOSS Lviv 2017
Data: 27-Abr-2017
Date of entry: 1-Abr-2018
Editora: Т.Б. Сорока
Place of the edition/event: Львів
Lviv
Temporal Coverage: 27-30 квітня 2017 року
27-30 April 2017
Number of pages: 3
Page range: 71-73
Start page: 71
End page: 73
Resumo: A 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.
URI: http://elartu.tntu.edu.ua/handle/lib/24469
ISBN: 978-966-2598-86-5
URL for reference material: https://youtu.be/6Ec0AhQ5BaM
http://www.infoworld.com/article/2853707/robotics/11-open-source-tools-machine-learning.html
http://scikit-learn.org/stable/
References (Ukraine): 1. 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
2. Scikit-learn: machine learning in Python. http://scikit-learn.org/stable/
3. Pedregosa F. et al. Scikit-learn: machine learning in Python // Journal of Machine Learning Research, № 12, 2011. – P. 2825–2830.
References (International): 1. 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
2. Scikit-learn: machine learning in Python. http://scikit-learn.org/stable/
3. Pedregosa F. et al. Scikit-learn: machine learning in Python, Journal of Machine Learning Research, No 12, 2011, P. 2825–2830.
Content type: Conference Abstract
Aparece nas colecções:FOSS Lviv 2017



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