NearestMeanClassifier: Nearest Mean Classifier

In RSSL: Implementations of Semi-Supervised Learning Approaches for Classification

Nearest Mean Classifier

Description

Implementation of the nearest mean classifier modeled. Classes are modeled as gaussians with equal, spherical 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. To get true nearest mean classification, set prior as a matrix with equal probability for all classes, i.e. matrix(0.5,2).

Usage

NearestMeanClassifier(X, y, prior = NULL, x_center = FALSE,
  scale = FALSE)

Arguments

X	matrix; Design matrix for labeled data
y	factor or integer vector; Label vector
prior	matrix; Class prior probabilities. If NULL, this will be estimated from the data
x_center	logical; Should the features be centered?
scale	logical; Should the features be normalized? (default: FALSE)

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, QuadraticDiscriminantClassifier, S4VM, SVM, SelfLearning, TSVM, USMLeastSquaresClassifier, WellSVM, svmlin()

RSSL documentation built on March 31, 2023, 7:27 p.m.