Trained SVM model as output from svmmaj.
The returning object consist of the following values:
The function specifications which has been called.
The regularization parameter of the penalty term which has been used.
The corresponding loss function value of the final solution.
Number of iterations needed to evaluate the algorithm.
The attribute matrix of dim(X) = c(n,k).
The vector of length n with the actual class labels.
These labels can be numeric [0 1] or two strings.
A vector of length n with the predicted
class labels of each object, derived from q.tilde
The attribute matrix X after standardization and
(if specified) spline transformation.
The applied normalization parameters
(see normalize).
The spline knots which has been used
(see isb).
Denotes the number of spline basis of
each explanatory variable in X.
The decomposition matrices used in estimating the model.
The hinge function which has been used
(see getHinge).
If identified, the beta parameters for the linear combination (only available for linear kernel).
A vector of length n with predicted values of
each object including the intercept.
Number of support vectors.
# S3 method for svmmaj
print(x, ...)# S3 method for svmmaj
summary(object, ...)
# S3 method for summary.svmmaj
print(x, ...)
# S3 method for svmmaj
plot(x, ...)
the svmmaj object as result of svmmaj
further arguments passed to or from other methods.
the svmmaj object as result of svmmaj