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| Halmozás× | Support Vector Machine (Osztályozás)× | |
|---|---|---|
| Tudományterület | Gépi tanulás | Gépi tanulás |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 1992 | 1995 |
| Megalkotó≠ | Wolpert, D.H. | Cortes, C. & Vapnik, V. |
| Típus≠ | Ensemble (heterogeneous meta-learning) | Maximum-margin classifier (kernel method) |
| Alapmű≠ | Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Alternatív nevek≠ | Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Kapcsolódó | 5 | 5 |
| Összefoglaló≠ | Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions. | 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. |
| ScholarGateAdatkészlet ↗ |
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