Salīdzināt metodes

	Lineārās diskriminanta analīze (LDA	K-tuvākie kaimiņi
Nozare	Statistika	Mašīnmācīšanās
Saime	Hypothesis test	Machine learning
Izcelsmes gads	1936	1967
Autors	Ronald A. Fisher	Cover, T.M. & Hart, P.E.
Tips	Parametric linear classifier / dimensionality reduction	Instance-based (non-parametric) learning
Pirmavots	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.	Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.
Citi nosaukumi	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning
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.	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.