পদ্ধতির তুলনা করুন

	অ্যাক্টিভ লার্নিং গাউসিয়ান প্রসেস	Bayesian Gaussian Process
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1992	1978–2006
প্রবর্তক	MacKay, D. J. C.	O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I.
ধরন	Bayesian active learning	Probabilistic kernel 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 regression, GPR, Gaussian process model, GP classifier
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 Bayesian Gaussian Process (GP) places a probability distribution directly over functions, using a kernel to encode similarity between inputs. After observing data, Bayes' rule converts this prior into a posterior that yields not just point predictions but calibrated uncertainty estimates at every new input — making it one of the most principled probabilistic models in machine learning.