পদ্ধতির তুলনা করুন

	এনসেম্বল সাপোর্ট ভেক্টর মেশিন	স্ট্যাকিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2000–2003	1992
প্রবর্তক	Kim, H.-C. et al.; Dietterich, T. G.	Wolpert, D.H.
ধরন	Ensemble of SVMs (bagging, voting, or stacking)	Ensemble (heterogeneous meta-learning)
মৌলিক উৎস	Kim, H.-C., Pang, S., Je, H.-M., Kim, D., & Bang, S. Y. (2002). Constructing support vector machine ensemble. Pattern Recognition, 36(12), 2757–2767.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
অপর নাম	Ensemble SVM, SVM ensemble, bagged SVM, SVM committee machine	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
সম্পর্কিত	5	5
সারসংক্ষেপ	Ensemble Support Vector Machine combines multiple independently trained SVM classifiers or regressors — each fitted on a different data partition, bootstrap sample, or feature subset — and aggregates their outputs via voting, averaging, or stacking. The approach mitigates the high computational cost and sensitivity to kernel hyperparameters inherent in a single large-scale SVM, while improving generalisation on complex or high-dimensional datasets.	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.