GPgym: A Remote Service Platform with Gaussian Process Regression for Online Learning
I tiakina i:
| I whakaputaina i: | arXiv.org (Dec 17, 2024), p. n/a |
|---|---|
| Kaituhi matua: | |
| Ētahi atu kaituhi: | |
| I whakaputaina: |
Cornell University Library, arXiv.org
|
| Ngā marau: | |
| Urunga tuihono: | Citation/Abstract Full text outside of ProQuest |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|
| Whakarāpopotonga: | 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 |
| Puna: | Engineering Database |