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| DBSCAN× | Random Forest× | Support Vector Machine (Classificazione)× | |
|---|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning | Machine learning |
| Anno di origine≠ | 1996 | 2001 | 1995 |
| Ideatore≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Breiman, L. | Cortes, C. & Vapnik, V. |
| Tipo≠ | Density-based clustering algorithm | Ensemble (bagging of decision trees) | Maximum-margin classifier (kernel method) |
| Fonte seminale≠ | 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 ↗ | 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 ↗ |
| Alias≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial 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 |
| Correlati≠ | 3 | 4 | 5 |
| Sintesi≠ | 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. | 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. |
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