Regularized Core Vector Machine with Central Vector-Angular Margin

For effective training on large datasets, we propose an alternate method to find the optimal vector, <em<d</em<, using the central vector-angular margin classifier (CAMC), which is based on the maximum vector-angular margin classifier. The CAMC can be considered to be equivalent to the corresponding minimum enclosing ball (MEB) problem. However, we have found that the MEB is very sensitive to the selection of the trade-off parameter, so we propose using a regularized core vector machine (RCVM). By connecting the CAMC to the RCVM, we obtain a central vector-angular margin regularized core vector machine (CAMCVM). Experimental results from the UCI datasets show that the CAMC has a better generalized performance, while the CAMCVM can be used for effective training on large datasets.