LinearTSVM | R Documentation |
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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