Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Иерархическая кластеризация× | Случайный лес× | Метод опорных векторов (классификация)× | |
|---|---|---|---|
| Область | Машинное обучение | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning | Machine learning |
| Год появления≠ | 1963 | 2001 | 1995 |
| Автор метода≠ | Ward, J. H. | Breiman, L. | Cortes, C. & Vapnik, V. |
| Тип≠ | Unsupervised clustering (agglomerative) | Ensemble (bagging of decision trees) | Maximum-margin classifier (kernel method) |
| Основополагающий источник≠ | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Другие названия≠ | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Связанные≠ | 4 | 4 | 5 |
| Сводка≠ | Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963. | 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. | The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data. |
| ScholarGateНабор данных ↗ |
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