Implementation of the SVM-Maj Algorithm
Returns the area under the curve value
Show the classification performance
Hinge error function of SVM-Maj
I-spline basis of each column of a given matrix
Transform a given data into I-splines
Normalize/standardize the columns of a matrix
Plot the hinge function
Plot the cross validation output
Plot the weights of all attributes from the trained SVM model
Out-of-Sample Prediction from Unseen Data.
Perform the transformation based on predefined settings
Print Svmmaj class
Print SVMMaj cross validation results
Plot the ROC curve of the predicted values
SVM-Maj Algorithm
k-fold Cross-Validation of SVM-Maj
Transform the data with normalization and/or spline basis
Returns transformed attributes
Implements the SVM-Maj algorithm to train data with support vector machine <doi:10.1007/s11634-008-0020-9>. This algorithm uses two efficient updates, one for linear kernel and one for the nonlinear kernel.