Võrdle meetodeid

	Reguleeritud veebiõpe	Stohhastiline gradiendilange (SGD)
Valdkond	Masinõpe	Masinõpe
Perekond	Machine learning	Machine learning
Tekkeaasta	2007–2013	1951
Looja	Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al.	Robbins, H. & Monro, S.
Tüüp	Online optimization framework with regularization	First-order iterative optimization algorithm
Algallikas	Xiao, L. (2010). Dual Averaging Methods for Regularized Stochastic and Online Optimization. Journal of Machine Learning Research, 11, 2543–2596.	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. The Annals of Mathematical Statistics, 22(3), 400–407.
Rööpnimetused	FTRL, Follow-the-Regularized-Leader, online regularized optimization, regularized dual averaging	SGD, online gradient descent, incremental gradient descent, mini-batch gradient descent
Seotud	6	3
Kokkuvõte	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.	Stochastic Gradient Descent (SGD) is a first-order iterative optimization algorithm, rooted in the stochastic approximation framework introduced by Robbins and Monro in 1951, that minimizes an objective function by updating model parameters using the gradient computed on a single randomly selected training example (or a small mini-batch) at each step. It is the core optimization engine behind modern machine learning and deep learning, enabling the training of models on datasets too large to fit in memory.