Linear CCCP Transductive SVM classifier
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.
LinearTSVM(X, y, X_u, C, Cstar, s = 0, x_center = FALSE, scale = FALSE, eps = 1e-06, verbose = FALSE, init = NULL)
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 |
Collobert, R. et al., 2006. Large scale transductive SVMs. Journal of Machine Learning Research, 7, pp.1687-1712.
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()
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