SVM: SVM Classifier

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

SVM Classifier

Description

Support Vector Machine implementation using the quadprog solver.

Usage

SVM(X, y, C = 1, kernel = NULL, scale = TRUE, intercept = FALSE,
  x_center = TRUE, eps = 1e-09)

Arguments

X	matrix; Design matrix for labeled data
y	factor or integer vector; Label vector
C	numeric; Cost variable
kernel	kernlab::kernel to use
scale	logical; Should the features be normalized? (default: FALSE)
intercept	logical; Whether an intercept should be included
x_center	logical; Should the features be centered?
eps	numeric; Small value to ensure positive definiteness of the matrix in the QP formulation

Details

This implementation will typically be slower and use more memory than the svmlib implementation in the e1071 package. It is, however, useful for comparisons with the TSVM implementation.

Value

S4 object of type SVM

See Also

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

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