विधियों की तुलना करें

	ऑनलाइन लीनियर रिग्रेशन	स्टोकेस्टिक ग्रेडिएंट डिसेंट (एसजीडी)
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1960 (LMS); 1950 (RLS formalization)	1951
प्रवर्तक	Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)	Robbins, H. & Monro, S.
प्रकार	Incremental supervised regression	First-order iterative optimization algorithm
मौलिक स्रोत	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.
उपनाम	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
संबंधित	6	3
सारांश	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.