विधियों की तुलना करें

	स्टैकिंग	सपोर्ट वेक्टर मशीन (वर्गीकरण)
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1992	1995
प्रवर्तक	Wolpert, D.H.	Cortes, C. & Vapnik, V.
प्रकार	Ensemble (heterogeneous meta-learning)	Maximum-margin classifier (kernel method)
मौलिक स्रोत	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.
उपनाम	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
संबंधित	5	5
सारांश	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.