Machine learningMachine learning
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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Sources
- 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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Active Learning Federated LearningActive Learning Self-supervised LearningApriori AlgorithmBayesian Online LearningEnsemble Online LearningOnline Active learningOnline Association RulesOnline Autoencoder Anomaly DetectionOnline BoostingOnline DBSCANOnline Decision TreeOnline Federated LearningOnline Few-shot LearningOnline Gaussian Mixture ModelOnline Gradient BoostingOnline HDBSCANOnline Isolation ForestOnline K-nearest neighborsOnline LightGBMOnline Linear RegressionOnline Logistic RegressionOnline Metric LearningOnline Naive BayesOnline Random ForestOnline Self-supervised LearningOnline Semi-supervised learningOnline Support Vector MachineOnline Transfer learningOnline Voting EnsembleRegularized Federated LearningRegularized Online LearningRobust Active LearningRobust Online LearningSemi-supervised Online Learning