Krahasoni metodat

	Analiza diskriminante lineare (LDA	Makineria e Vektorëve Mbështetës (Klasifikimi)
Fusha	Statistikë	Mësimi i makinës
Familja	Hypothesis test	Machine learning
Viti i origjinës	1936	1995
Krijuesi	Ronald A. Fisher	Cortes, C. & Vapnik, V.
Lloji	Parametric linear classifier / dimensionality reduction	Maximum-margin classifier (kernel method)
Burimi themelues	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.
Emërtime të tjera	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Të lidhura	7	5
Përmbledhja	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.