方法证据记录
Regularized Online Learning
Regularized online learning extends the online learning paradigm by incorporating a regularization penalty into each weight update, controlling model complexity while processing data one example at a time. Algorithms such as Follow-the-Regularized-Leader (FTRL) and Regularized Dual Averaging (RDA) make this approach practical at scale, enabling sparse, well-calibrated models on streaming data.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Regularized Online Learning (Online Learning with Regularization)
分类方法记录 · ml-model / machine-learning
- Xiao, L. (2010). Dual Averaging Methods for Regularized Stochastic and Online Optimization. Journal of Machine Learning Research, 11, 2543–2596. · URL
- Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. · DOI 10.1561/2200000018
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