So sánh phương pháp

	Máy Vectơ Hỗ trợ (Phân loại)	Rừng ngẫu nhiên
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1995	2001
Người khởi xướng	Cortes, C. & Vapnik, V.	Breiman, L.
Loại	Maximum-margin classifier (kernel method)	Ensemble (bagging of decision trees)
Công trình gốc	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Tên gọi khác	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Liên quan	5	4
Tóm tắt	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.