পদ্ধতির তুলনা করুন

	গ্রেডিয়েন্ট বুস্টিং	Semi-supervised Boosting
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2001	1999–2009
প্রবর্তক	Friedman, J. H.	Mallapragada, P. K.; Bennett, K. P.; and others
ধরন	Ensemble (sequential boosting of decision trees)	Semi-supervised ensemble method
মৌলিক উৎস	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Mallapragada, P. K., Jin, R., Jain, A. K., & Liu, Y. (2009). SemiBoost: Boosting for Semi-supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2000–2014.
অপর নাম	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting
সম্পর্কিত	5	5
সারসংক্ষেপ	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	Semi-supervised Boosting is an ensemble learning paradigm that extends classical boosting algorithms — such as AdaBoost — to exploit both labeled and unlabeled data. By propagating label information through a similarity structure over unlabeled instances, it trains stronger classifiers than supervised boosting alone when labeled data are scarce.