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

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Xuất bản năm:	arXiv.org (Dec 17, 2024), p. n/a
Tác giả chính:	Dai, Xiaobing
Tác giả khác:	Yang, Zewen
Được phát hành:	Cornell University Library, arXiv.org
Những chủ đề:	Gaussian process Algorithms Python Machine learning Programming languages
Truy cập trực tuyến:	Citation/Abstract Full text outside of ProQuest
Các nhãn:	Thêm thẻ Không có thẻ, Là người đầu tiên thẻ bản ghi này!

Miêu tả
Bài tóm tắt:	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.
số ISSN:	2331-8422
Nguồn:	Engineering Database