Trained SVM model as output from svmmaj.
The returning object consist of the following values:
call The function specifications which has been called.
lambda The regularization parameter of the penalty term which has been used.
loss The corresponding loss function value of the final solution.
iteration Number of iterations needed to evaluate the algorithm.
X The attribute matrix of dim(X) = c(n,k).
y The vector of length n with the actual class labels.
These labels can be numeric [0 1] or two strings.
classes A vector of length n with the predicted
class labels of each object, derived from q.tilde
Xtrans The attribute matrix X after standardization and
(if specified) spline transformation.
norm.param The applied normalization parameters
(see normalize).
splineInterval The spline knots which has been used
(see isb).
splineLengthDenotes the number of spline basis of
each explanatory variable in X.
methodThe decomposition matrices used in estimating the model.
hinge The hinge function which has been used
(see getHinge).
beta If identified, the beta parameters for the linear combination (only available for linear kernel).
q A vector of length n with predicted values of
each object including the intercept.
nSV 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