Salīdzināt metodes

	Tiešsaistes lineārā regresija	Stohastiskā gradienta metode (SGD)
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1960 (LMS); 1950 (RLS formalization)	1951
Autors	Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)	Robbins, H. & Monro, S.
Tips	Incremental supervised regression	First-order iterative optimization algorithm
Pirmavots	Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. The Annals of Mathematical Statistics, 22(3), 400–407.
Citi nosaukumi	incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression	SGD, online gradient descent, incremental gradient descent, mini-batch gradient descent
Saistītās	6	3
Kopsavilkums	Online Linear Regression fits a linear model one observation at a time, updating weights incrementally as each new data point arrives. Unlike batch least-squares, it never needs to store or re-process the full dataset, making it the natural choice for streaming data, very large datasets, and environments where the data-generating process can shift over time.	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.