Σύγκριση μεθόδων

	Μπεϋζιανή Ημι-εποπτευόμενη Μάθηση	Διαδικασία Γκάους
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2003–2006	2006 (book); roots in Kriging, 1951)
Δημιουργός	Chapelle, Scholkopf & Zien; Zhu, Ghahramani & Lafferty	Rasmussen, C. E. & Williams, C. K. I.
Τύπος	Probabilistic semi-supervised framework	Probabilistic non-parametric model
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	Bayesian SSL, probabilistic semi-supervised learning, generative semi-supervised model, Bayesian transductive learning	GP, Gaussian Process Regression, GPR, Kriging
Συναφείς	6	3
Σύνοψη	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.