পদ্ধতির তুলনা করুন

	রেগুলারাইজড অনলাইন লার্নিং	স্টোকাস্টিক গ্রেডিয়েন্ট ডিসেন্ট (SGD)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2007–2013	1951
প্রবর্তক	Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al.	Robbins, H. & Monro, S.
ধরন	Online optimization framework with regularization	First-order iterative optimization algorithm
মৌলিক উৎস	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.
অপর নাম	FTRL, Follow-the-Regularized-Leader, online regularized optimization, regularized dual averaging	SGD, online gradient descent, incremental gradient descent, mini-batch gradient descent
সম্পর্কিত	6	3
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.	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.