পদ্ধতির তুলনা করুন

	অনলাইন গাউসিয়ান প্রসেস	স্টোকাস্টিক গ্রেডিয়েন্ট ডিসেন্ট (SGD)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2002	1951
প্রবর্তক	Csató, L. & Opper, M.	Robbins, H. & Monro, S.
ধরন	Bayesian nonparametric model (sequential/online)	First-order iterative optimization algorithm
মৌলিক উৎস	Csató, L. & Opper, M. (2002). Sparse on-line Gaussian processes. Neural Computation, 14(3), 641–668.	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. The Annals of Mathematical Statistics, 22(3), 400–407.
অপর নাম	OGP, sparse online GP, sequential Gaussian process, incremental Gaussian process	SGD, online gradient descent, incremental gradient descent, mini-batch gradient descent
সম্পর্কিত	3	3
সারসংক্ষেপ	Online Gaussian Process (OGP) extends the Bayesian nonparametric GP framework to streaming or sequentially arriving data. Instead of recomputing the full GP posterior from scratch as each observation arrives, OGP maintains a compact summary — a sparse set of inducing points — and updates it incrementally, making probabilistic regression and classification feasible in real-time and large-scale settings.	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.