กรุณาใช้ตัวระบุนี้เพื่ออ้างอิงหรือเชื่อมต่อรายการนี้: http://elartu.tntu.edu.ua/handle/lib/24469

ชื่อเรื่อง: Выкарыстанне бібліятэкі scikit-learn ў задачы класіфікацыі рухаў трэкбола
ชื่อเรื่องอื่นๆ: Використання бібліотеки scikit-learn в завданні класифікації рухів трекбола
Using scikit-learn library classification problem of trackball movements
ผู้แต่ง: 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
วันที่เผยแพร่: 27-เมษ-2017
Date of entry: 1-เมษ-2018
สำนักพิมพ์: 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
บทคัดย่อ: 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
ปรากฏในกลุ่มข้อมูล:FOSS Lviv 2017

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