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	Wzmocnienie	Klasteryzacja K-średnich
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	1990–1997	1967
Twórca	Schapire, R. E.; Freund, Y.	MacQueen, J.
Typ	Sequential ensemble (iterative reweighting)	Partitional clustering (centroid-based)
Źródło pierwotne	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
Inne nazwy	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Pokrewne	6	3
Podsumowanie	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.