Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| DBSCAN× | Метод опорных векторов (классификация)× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1996 | 1995 |
| Автор метода≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Cortes, C. & Vapnik, V. |
| Тип≠ | Density-based clustering algorithm | Maximum-margin classifier (kernel method) |
| Основополагающий источник≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231. link ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Другие названия≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Связанные≠ | 3 | 5 |
| Сводка≠ | DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes. | 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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