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	बायेसियन अर्ध-पर्यवेक्षित शिक्षण (Bayesian Semi-supervised Learning)	गॉसियन प्रक्रिया
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2003–2006	2006 (book); roots in Kriging, 1951)
प्रवर्तक	Chapelle, Scholkopf & Zien; Zhu, Ghahramani & Lafferty	Rasmussen, C. E. & Williams, C. K. I.
प्रकार	Probabilistic semi-supervised framework	Probabilistic non-parametric model
मौलिक स्रोत	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
उपनाम	Bayesian SSL, probabilistic semi-supervised learning, generative semi-supervised model, Bayesian transductive learning	GP, Gaussian Process Regression, GPR, Kriging
संबंधित	6	3
सारांश	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.