Implementation of the Linear Support Vector Classifier. Can be solved in the Dual formulation, which is equivalent to SVM or the Primal formulation.
LinearSVM(X, y, C = 1, method = "Dual", scale = TRUE, eps = 1e-09,
reltol = 1e-13, maxit = 100)S4 object of type LinearSVM
matrix; Design matrix for labeled data
factor or integer vector; Label vector
Cost variable
Estimation procedure c("Dual","Primal","BGD")
Whether a z-transform should be applied (default: TRUE)
Small value to ensure positive definiteness of the matrix in QP formulation
relative tolerance using during BFGS optimization
Maximum number of iterations for BFGS optimization
Other RSSL classifiers:
EMLeastSquaresClassifier,
EMLinearDiscriminantClassifier,
GRFClassifier,
ICLeastSquaresClassifier,
ICLinearDiscriminantClassifier,
KernelLeastSquaresClassifier,
LaplacianKernelLeastSquaresClassifier(),
LaplacianSVM,
LeastSquaresClassifier,
LinearDiscriminantClassifier,
LinearTSVM(),
LogisticLossClassifier,
LogisticRegression,
MCLinearDiscriminantClassifier,
MCNearestMeanClassifier,
MCPLDA,
MajorityClassClassifier,
NearestMeanClassifier,
QuadraticDiscriminantClassifier,
S4VM,
SVM,
SelfLearning,
TSVM,
USMLeastSquaresClassifier,
WellSVM,
svmlin()