LogisticLossClassifier: Logistic Loss Classifier

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

Logistic Loss Classifier

Description

Find the linear classifier which minimizing the logistic loss on the training set, optionally using L2 regularization.

Usage

LogisticLossClassifier(X, y, lambda = 0, intercept = TRUE, scale = FALSE,
  init = NA, x_center = FALSE, ...)

Arguments

X	Design matrix, intercept term is added within the function
y	Vector with class assignments
lambda	Regularization parameter used for l2 regularization
intercept	TRUE if an intercept should be added to the model
scale	If TRUE, apply a z-transform to all observations in X and X_u before running the regression
init	Starting parameter vector for gradient descent
x_center	logical; Whether the feature vectors should be centered
...	additional arguments

Value

S4 object with the following slots

w	the weight vector of the linear classifier
classnames	vector with names of the classes

See Also

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

RSSL documentation built on May 29, 2024, 2:38 a.m.