השוואת שיטות

	ניתוח דיסקרימיננטי ליניארי (LDA	מכונת וקטורים תומכים (סיווג)
תחום	סטטיסטיקה	למידת מכונה
משפחה	Hypothesis test	Machine learning
שנת המקור	1936	1995
הוגה השיטה	Ronald A. Fisher	Cortes, C. & Vapnik, V.
סוג	Parametric linear classifier / dimensionality reduction	Maximum-margin classifier (kernel method)
מקור מכונן	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
כינויים	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
קשורות	7	5
תקציר	Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA.	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.