Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель гауссовских смесей× | Случайный лес× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1977 | 2001 |
| Автор метода≠ | Dempster, Laird & Rubin (EM algorithm) | Breiman, L. |
| Тип≠ | Probabilistic (soft) clustering — mixture model | Ensemble (bagging of decision trees) |
| Основополагающий источник≠ | Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–22. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Другие названия≠ | Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Связанные | 4 | 4 |
| Сводка≠ | A Gaussian Mixture Model is a probabilistic clustering method that models the data as a weighted mixture of several Gaussian distributions, fitted with the Expectation–Maximization algorithm formalized by Dempster, Laird & Rubin in 1977. It is a generalization of K-means in which each cluster can take its own shape, size, and orientation. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateНабор данных ↗ |
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