পদ্ধতির তুলনা করুন

	রেগুলারাইজড অনলাইন লার্নিং	নিয়ন্ত্রিত রৈখিক নির্ভরণ (Regularized Linear Regression)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2007–2013	1970–2005
প্রবর্তক	Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al.	Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)
ধরন	Online optimization framework with regularization	Penalized linear model
মৌলিক উৎস	Xiao, L. (2010). Dual Averaging Methods for Regularized Stochastic and Online Optimization. Journal of Machine Learning Research, 11, 2543–2596.	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
অপর নাম	FTRL, Follow-the-Regularized-Leader, online regularized optimization, regularized dual averaging	Ridge regression, Lasso regression, Elastic Net regression, penalized regression
সম্পর্কিত	6	4
সারসংক্ষেপ	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.	Regularized linear regression adds a penalty term to the ordinary least-squares objective, shrinking or zeroing out coefficients to reduce overfitting and handle multicollinearity. The three main variants — Ridge (L2 penalty), Lasso (L1 penalty), and Elastic Net (combined L1+L2) — make linear regression usable even when features outnumber observations or predictors are highly correlated.