Salīdzināt metodes

	Lineārās diskriminanta analīze (LDA	Atbalsta vektoru mašīna (klasifikācija)
Nozare	Statistika	Mašīnmācīšanās
Saime	Hypothesis test	Machine learning
Izcelsmes gads	1936	1995
Autors	Ronald A. Fisher	Cortes, C. & Vapnik, V.
Tips	Parametric linear classifier / dimensionality reduction	Maximum-margin classifier (kernel method)
Pirmavots	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.
Citi nosaukumi	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Saistītās	7	5
Kopsavilkums	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.