Abstract
This chapter presents an assessment of the potential of Online Machine Learning (OML) for practitioners. The results of the studies are summarized and discussed and concrete recommendations for OML practice are given. The importance of a suitable comparison methodology for Batch Machine Learning (BML) and OML methods is highlighted to avoid “comparing apples to oranges”. We also point out the great potential of OML that is available through the development of the open-source software River.
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References
Bartz, E., et al. (2022). Hyperparameter tuning for machine and deep learning with R-A practical guide. Springer.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Bartz-Beielstein, T., Bartz, E. (2024). Summary and Outlook. In: Bartz, E., Bartz-Beielstein, T. (eds) Online Machine Learning. Machine Learning: Foundations, Methodologies, and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-99-7007-0_11
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DOI: https://doi.org/10.1007/978-981-99-7007-0_11
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