قارن الطرق

	التعلم شبه المُشرف البيزي	Gaussian Process
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2003–2006	2006 (book); roots in Kriging, 1951)
صاحب الطريقة	Chapelle, Scholkopf & Zien; Zhu, Ghahramani & Lafferty	Rasmussen, C. E. & Williams, C. K. I.
النوع	Probabilistic semi-supervised framework	Probabilistic non-parametric model
المصدر التأسيسي	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
الأسماء البديلة	Bayesian SSL, probabilistic semi-supervised learning, generative semi-supervised model, Bayesian transductive learning	GP, Gaussian Process Regression, GPR, Kriging
ذات صلة	6	3
الملخص	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.