Machine learningMachine learning
正则化在线学习
正则化在线学习通过在每次权重更新中引入正则化惩罚来扩展在线学习范式，从而在逐个处理数据示例的同时控制模型复杂性。诸如“跟随正则化领导者”（FTRL）和“正则化双重平均”（RDA）等算法使这种方法在大规模应用中变得实用，能够在流式数据上生成稀疏、校准良好的模型。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Xiao, L. (2010). Dual Averaging Methods for Regularized Stochastic and Online Optimization. Journal of Machine Learning Research, 11, 2543–2596. link ↗
- Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI: 10.1561/2200000018 ↗
如何引用本页
ScholarGate. (2026, June 3). Regularized Online Learning (Online Learning with Regularization). ScholarGate. https://scholargate.app/zh/machine-learning/regularized-online-learning
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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