Confronta i metodi

	Apprendimento Bayesiano Semi-Supervisionato	Processo Gaussiano
Campo	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning
Anno di origine	2003–2006	2006 (book); roots in Kriging, 1951)
Ideatore	Chapelle, Scholkopf & Zien; Zhu, Ghahramani & Lafferty	Rasmussen, C. E. & Williams, C. K. I.
Tipo	Probabilistic semi-supervised framework	Probabilistic non-parametric model
Fonte seminale	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Alias	Bayesian SSL, probabilistic semi-supervised learning, generative semi-supervised model, Bayesian transductive learning	GP, Gaussian Process Regression, GPR, Kriging
Correlati	6	3
Sintesi	Bayesian semi-supervised learning is a probabilistic framework that uses both a small labeled dataset and a larger pool of unlabeled observations to infer model parameters and make predictions. By treating missing labels as latent variables and placing priors over parameters, it naturally quantifies uncertainty while leveraging unlabeled data to improve generalization.	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.