The main features of this package are: implementation of the SVM-Maj majorization algorithm for SVMs, handling of nonlinearity through splines and kernels, the ability to handle several error functions (among other the classic hinge, quadratic hinge and Huber hinge error).
The main functions of the package are svmmaj, which
estimates the SVM, and svmmajcrossval, which performs a
grid search of k-fold cross validations using
SVM-Maj to find the combination of input values, (such
as lambda and degree in the case of a
polynomial kernel) giving the best prediction performance.
The former function requires the n x k attribute matrix
X and the n x 1 vector y with
class labels. Apart from the data objects, other parameter input values
can be given as input to tune the model: - lambda, -
hinge, - weights.obs, - scale,
and - parameters for nonlinearities and settings of the algorithm
itself.
For example,
svmmaj(X, y, lambda = 2, hinge = "quadratic", scale = "interval")runs the SVM model with lambda = 2, using a quadratic
hinge and for each attribute, the values are scaled to the interval
[0,1]. The function svmmajcrossval uses the same parameter
input values and additionally the parameters to be used as grid points
of the k-fold cross validation. These parameters should be
given in the list object search.grid, e.g.,
svmmajcrossval(X, y, search.grid = list(lambda = c(1, 2, 4)))