قارن الطرق

	تحليل التمييز الخطي (LDA	آلة المتجهات الداعمة (التصنيف)
المجال	الإحصاء	تعلم الآلة
العائلة	Hypothesis test	Machine learning
سنة النشأة	1936	1995
صاحب الطريقة	Ronald A. Fisher	Cortes, C. & Vapnik, V.
النوع	Parametric linear classifier / dimensionality reduction	Maximum-margin classifier (kernel method)
المصدر التأسيسي	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.
الأسماء البديلة	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
ذات صلة	7	5
الملخص	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.