GPgym: A Remote Service Platform with Gaussian Process Regression for Online Learning

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Podrobná bibliografie
Vydáno v:	arXiv.org (Dec 17, 2024), p. n/a
Hlavní autor:	Dai, Xiaobing
Další autoři:	Yang, Zewen
Vydáno:	Cornell University Library, arXiv.org
Témata:	Gaussian process Algorithms Python Machine learning Programming languages
On-line přístup:	Citation/Abstract Full text outside of ProQuest
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Abstrakt:	Machine learning is now widely applied across various domains, including industry, engineering, and research. While numerous mature machine learning models have been open-sourced on platforms like GitHub, their deployment often requires writing scripts in specific programming languages, such as Python, C++, or MATLAB. This dependency on particular languages creates a barrier for professionals outside the field of machine learning, making it challenging to integrate these algorithms into their workflows. To address this limitation, we propose GPgym, a remote service node based on Gaussian process regression. GPgym enables experts from diverse fields to seamlessly and flexibly incorporate machine learning techniques into their existing specialized software, without needing to write or manage complex script code.
ISSN:	2331-8422
Zdroj:	Engineering Database