Dossier de preuve de méthode

Online Learning

Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.

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Online Learning (Sequential / Incremental Machine Learning)

Dossier de méthode taxonomique · ml-model / machine-learning

Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. · DOI 10.1561/2200000018
Cesa-Bianchi, N. & Lugosi, G. (2006). Prediction, Learning, and Games. Cambridge University Press. · ISBN 978-0-521-84108-5

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Méthodes apparentées

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Same method familyActive Learningmachine-suggested · Relational suggestion, not evidence.Same method familyFederated Learningmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketFew-shot Learningmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSelf-supervised Learningmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSemi-supervised Learningmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketTransfer Learningmachine-suggested · Relational suggestion, not evidence.