পদ্ধতির তুলনা করুন

	অ্যাক্টিভ লার্নিং গাউসিয়ান প্রসেস	গাউসিয়ান প্রসেস
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1992	2006 (book); roots in Kriging, 1951)
প্রবর্তক	MacKay, D. J. C.	Rasmussen, C. E. & Williams, C. K. I.
ধরন	Bayesian active learning	Probabilistic non-parametric model
মৌলিক উৎস	MacKay, D. J. C. (1992). Information-based objective functions for active data selection. Neural Computation, 4(4), 590–604.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
অপর নাম	GP active learning, Gaussian process active learning, GP-AL, Bayesian active learning with GP	GP, Gaussian Process Regression, GPR, Kriging
সম্পর্কিত	4	3
সারসংক্ষেপ	Active Learning Gaussian Process (GP-AL) combines a Gaussian process probabilistic model with an active learning query strategy, using the GP's posterior uncertainty to select the most informative unlabeled examples for labeling. This iterative approach minimizes labeling effort while maximizing predictive accuracy, making it ideal when labeled data is scarce or expensive to obtain.	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.