Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Arbore de decizie× | Mașina cu Vectori Suport (Clasificare)× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 1984 | 1995 |
| Autorul original≠ | Breiman, Friedman, Olshen & Stone | Cortes, C. & Vapnik, V. |
| Tip≠ | Recursive partitioning (if-then rules) | Maximum-margin classifier (kernel method) |
| Sursa seminală≠ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Denumiri alternative≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Înrudite | 5 | 5 |
| Rezumat≠ | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. | 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. |
| ScholarGateSet de date ↗ |
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