השוואת שיטות

	מכונת וקטורים תומכים (סיווג)	שיטת השכנים הקרובים ביותר (K-Nearest Neighbors)	יער אקראי
תחום	למידת מכונה	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning	Machine learning
שנת המקור	1995	1967	2001
הוגה השיטה	Cortes, C. & Vapnik, V.	Cover, T.M. & Hart, P.E.	Breiman, L.
סוג	Maximum-margin classifier (kernel method)	Instance-based (non-parametric) learning	Ensemble (bagging of decision trees)
מקור מכונן	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.	Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
כינויים	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
קשורות	5	5	4
תקציר	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.	K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values.	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.