Comparar métodos

	Processo Gaussiano Regularizado	Máquina de Vetores de Suporte Regularizada
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2006 (canonical formulation); kernel regularization roots 1990s	1995–2004
Autor original	Rasmussen, C. E. & Williams, C. K. I.	Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM)
Tipo	Probabilistic kernel model with regularization	Regularized discriminative classifier / regressor
Fonte seminal	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.
Outros nomes	Regularized GP, GP with noise regularization, sparse regularized Gaussian process, regularized Gaussian process regression	Regularized SVM, L1-SVM, L2-SVM, penalized SVM
Relacionados	4	4
Resumo	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.