Comparer des méthodes

	k-Plus-Proches Voisins Régularisé	Processus Gaussien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1967–2000s	2006 (book); roots in Kriging, 1951)
Auteur d'origine	Extends Cover & Hart (1967); regularization formulations developed through kernel smoothing literature	Rasmussen, C. E. & Williams, C. K. I.
Type	Instance-based / lazy learner with regularization	Probabilistic non-parametric model
Source fondatrice	Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Alias	regularized kNN, kernel-weighted kNN, distance-regularized nearest neighbors, kNN with regularization	GP, Gaussian Process Regression, GPR, Kriging
Apparentées	4	3
Résumé	Regularized k-Nearest Neighbors (kNN) extends the classical nearest-neighbor algorithm by incorporating regularization mechanisms — most commonly kernel-based distance weighting or bandwidth control — that smooth predictions, reduce sensitivity to the choice of k, and lower variance. The result is a more stable and better-calibrated instance-based learner for classification and regression tasks on tabular data.	A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks.