So sánh phương pháp

	Stacking	Máy Vectơ Hỗ trợ (Phân loại)
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1992	1995
Người khởi xướng	Wolpert, D.H.	Cortes, C. & Vapnik, V.
Loại	Ensemble (heterogeneous meta-learning)	Maximum-margin classifier (kernel method)
Công trình gốc	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.
Tên gọi khác	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Liên quan	5	5
Tóm tắt	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.