পদ্ধতির তুলনা করুন

	নিয়ন্ত্রিত আধা-তত্ত্বাবধায়ক শিখন	গাউসিয়ান প্রসেস
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2006	2006 (book); roots in Kriging, 1951)
প্রবর্তক	Belkin, M.; Niyogi, P.; Sindhwani, V.	Rasmussen, C. E. & Williams, C. K. I.
ধরন	Regularized learning paradigm	Probabilistic non-parametric model
মৌলিক উৎস	Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399–2434.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
অপর নাম	manifold regularization, graph-regularized SSL, semi-supervised regularization, Laplacian regularization	GP, Gaussian Process Regression, GPR, Kriging
সম্পর্কিত	6	3
সারসংক্ষেপ	Regularized semi-supervised learning adds explicit geometric or graph-based penalty terms to a semi-supervised objective so that the decision function varies smoothly over the data manifold. Pioneered through manifold regularization (Belkin, Niyogi & Sindhwani, 2006), it exploits the structure of both labeled and unlabeled examples to learn more accurate models than supervised regularization alone when labeled data are scarce.	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.