Σύγκριση μεθόδων

	Stacking	Μηχανή Υποστήριξης Διανυσμάτων (Ταξινόμηση)
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1992	1995
Δημιουργός	Wolpert, D.H.	Cortes, C. & Vapnik, V.
Τύπος	Ensemble (heterogeneous meta-learning)	Maximum-margin classifier (kernel method)
Θεμελιώδης πηγή	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Εναλλακτικές ονομασίες	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Συναφείς	5	5
Σύνοψη	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.