LinearTSVM: Linear CCCP Transductive SVM classifier

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

Linear CCCP Transductive SVM classifier

Description

Implementation for the Linear TSVM. This method is mostly for debugging purposes and does not allow for the balancing constraint or kernels, like the TSVM function.

Usage

LinearTSVM(X, y, X_u, C, Cstar, s = 0, x_center = FALSE, scale = FALSE,
  eps = 1e-06, verbose = FALSE, init = NULL)

Arguments

X	matrix; Design matrix, intercept term is added within the function
y	vector; Vector or factor with class assignments
X_u	matrix; Design matrix of the unlabeled data, intercept term is added within the function
C	numeric; Cost parameter of the SVM
Cstar	numeric; Cost parameter of the unlabeled objects
s	numeric; parameter controlling the loss function of the unlabeled objects
x_center	logical; Should the features be centered?
scale	logical; If TRUE, apply a z-transform to all observations in X and X_u before running the regression
eps	numeric; Convergence criterion
verbose	logical; print debugging messages (default: FALSE)
init	numeric; Initial classifier parameters to start the convex concave procedure

References

Collobert, R. et al., 2006. Large scale transductive SVMs. Journal of Machine Learning Research, 7, pp.1687-1712.

See Also

Other RSSL classifiers: EMLeastSquaresClassifier, EMLinearDiscriminantClassifier, GRFClassifier, ICLeastSquaresClassifier, ICLinearDiscriminantClassifier, KernelLeastSquaresClassifier, LaplacianKernelLeastSquaresClassifier(), LaplacianSVM, LeastSquaresClassifier, LinearDiscriminantClassifier, LinearSVM, LogisticLossClassifier, 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.