QuadraticDiscriminantClassifier {RSSL}

Quadratic Discriminant Classifier

Description

Implementation of the quadratic discriminant classifier. Classes are modeled as Gaussians with different covariance matrices. The optimal covariance matrix and means for the classes are found using maximum likelihood, which, in this case, has a closed form solution.

Usage

QuadraticDiscriminantClassifier(X, y, prior = NULL, scale = FALSE, ...)

Arguments

X

matrix; Design matrix for labeled data

y

factor or integer vector; Label vector

prior

A matrix with class prior probabilities. If NULL, this will be estimated from the data

scale

logical; Should the features be normalized? (default: FALSE)

...

Not used

Value

S4 object of class LeastSquaresClassifier with the following slots:

modelform

weight vector

prior

the prior probabilities of the classes

mean

the estimates means of the classes

sigma

The estimated covariance matrix

classnames

a vector with the classnames for each of the classes

scaling

scaling object used to transform new observations

See Also

Other RSSL classifiers: EMLeastSquaresClassifier, EMLinearDiscriminantClassifier, GRFClassifier, ICLeastSquaresClassifier, ICLinearDiscriminantClassifier, KernelLeastSquaresClassifier, LaplacianKernelLeastSquaresClassifier(), LaplacianSVM, LeastSquaresClassifier, LinearDiscriminantClassifier, LinearSVM, LinearTSVM(), LogisticLossClassifier, LogisticRegression, MCLinearDiscriminantClassifier, MCNearestMeanClassifier, MCPLDA, MajorityClassClassifier, NearestMeanClassifier, S4VM, SVM, SelfLearning, TSVM, USMLeastSquaresClassifier, WellSVM, svmlin()