Módszerek összehasonlítása

	Regularizált Gauss-folyamat	Szabályozott Támogatásvektoros Gépek
Tudományterület	Gépi tanulás	Gépi tanulás
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	2006 (canonical formulation); kernel regularization roots 1990s	1995–2004
Megalkotó	Rasmussen, C. E. & Williams, C. K. I.	Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM)
Típus	Probabilistic kernel model with regularization	Regularized discriminative classifier / regressor
Alapmű	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9	Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
Alternatív nevek	Regularized GP, GP with noise regularization, sparse regularized Gaussian process, regularized Gaussian process regression	Regularized SVM, L1-SVM, L2-SVM, penalized SVM
Kapcsolódó	4	4
Összefoglaló	A Regularized Gaussian Process (GP) is a probabilistic kernel-based model that places a prior over functions and explicitly controls overfitting through a noise regularization parameter — the observation noise variance — that prevents the model from memorizing training labels. It produces calibrated uncertainty estimates alongside predictions, making it uniquely suited to small or expensive datasets where knowing how confident the model is matters as much as the prediction itself.	Regularized Support Vector Machine extends the classic SVM by explicitly controlling the trade-off between margin maximization and training error through an L1 or L2 penalty parameter. The soft-margin formulation introduced by Cortes and Vapnik in 1995 is itself a regularized model, and later L1-SVM variants additionally promote feature sparsity, enabling automatic variable selection in high-dimensional settings.