Awesome-OL: An Extensible Toolkit for Online Learning
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912504302534656 |
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| author | Liu, Zeyi Hu, Songqiao Han, Pengyu Liu, Jiaming He, Xiao |
| author_facet | Liu, Zeyi Hu, Songqiao Han, Pengyu Liu, Jiaming He, Xiao |
| contents | In recent years, online learning has attracted increasing attention due to its adaptive capability to process streaming and non-stationary data. To facilitate algorithm development and practical deployment in this area, we introduce Awesome-OL, an extensible Python toolkit tailored for online learning research. Awesome-OL integrates state-of-the-art algorithm, which provides a unified framework for reproducible comparisons, curated benchmark datasets, and multi-modal visualization. Built upon the scikit-multiflow open-source infrastructure, Awesome-OL emphasizes user-friendly interactions without compromising research flexibility or extensibility. The source code is publicly available at: https://github.com/liuzy0708/Awesome-OL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20144 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Awesome-OL: An Extensible Toolkit for Online Learning Liu, Zeyi Hu, Songqiao Han, Pengyu Liu, Jiaming He, Xiao Machine Learning Artificial Intelligence In recent years, online learning has attracted increasing attention due to its adaptive capability to process streaming and non-stationary data. To facilitate algorithm development and practical deployment in this area, we introduce Awesome-OL, an extensible Python toolkit tailored for online learning research. Awesome-OL integrates state-of-the-art algorithm, which provides a unified framework for reproducible comparisons, curated benchmark datasets, and multi-modal visualization. Built upon the scikit-multiflow open-source infrastructure, Awesome-OL emphasizes user-friendly interactions without compromising research flexibility or extensibility. The source code is publicly available at: https://github.com/liuzy0708/Awesome-OL. |
| title | Awesome-OL: An Extensible Toolkit for Online Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.20144 |