Salīdzināt metodes

	Tiešsaistes Gausa process	Stohastiskā gradienta metode (SGD)
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2002	1951
Autors	Csató, L. & Opper, M.	Robbins, H. & Monro, S.
Tips	Bayesian nonparametric model (sequential/online)	First-order iterative optimization algorithm
Pirmavots	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.
Citi nosaukumi	OGP, sparse online GP, sequential Gaussian process, incremental Gaussian process	SGD, online gradient descent, incremental gradient descent, mini-batch gradient descent
Saistītās	3	3
Kopsavilkums	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.