Man pages for SVMMaj
Implementation of the SVM-Maj Algorithm
| auc | Returns the area under the curve value |
| AusCredit | Australian Credit Approval Dataset |
| classification | Show the classification performance |
| diabetes | Pima Indians Diabetes Data Set |
| getHinge | Hinge error function of SVM-Maj |
| isb | I-spline basis of each column of a given matrix |
| isplinebasis | Transform a given data into I-splines |
| normalize | Normalize/standardize the columns of a matrix |
| plot.hinge | Plot the hinge function |
| plot.svmmajcrossval | Plot the cross validation output |
| plotWeights | Plot the weights of all attributes from the trained SVM model |
| predict.svmmaj | Out-of-Sample Prediction from Unseen Data. |
| predict.transDat | Perform the transformation based on predefined settings |
| print.svmmaj | Print Svmmaj class |
| print.svmmajcrossval | Print SVMMaj cross validation results |
| roccurve | Plot the ROC curve of the predicted values |
| supermarket1996 | Supermarket data 1996 |
| svmmaj | SVM-Maj Algorithm |
| svmmajcrossval | k-fold Cross-Validation of SVM-Maj |
| transformdata | Transform the data with normalization and/or spline basis |
| voting | Congressional Voting Records Data Set |
| X.svmmaj | Returns transformed attributes |
