방법 비교

	온라인 가우시안 프로세스	확률적 경사 하강법(Stochastic Gradient Descent, SGD)
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2002	1951
창시자	Csató, L. & Opper, M.	Robbins, H. & Monro, S.
유형	Bayesian nonparametric model (sequential/online)	First-order iterative optimization algorithm
원전	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.
별칭	OGP, sparse online GP, sequential Gaussian process, incremental Gaussian process	SGD, online gradient descent, incremental gradient descent, mini-batch gradient descent
관련	3	3
요약	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.