Сравнение на методи

	Бустинг	Клъстериране с К-средни
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	1990–1997	1967
Създател	Schapire, R. E.; Freund, Y.	MacQueen, J.
Тип	Sequential ensemble (iterative reweighting)	Partitional clustering (centroid-based)
Основополагащ източник	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.
Други названия	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Свързани	6	3
Резюме	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.