The process demonstrates the influence of the number of training examples on the performance of the linear SVM in the case of the Adult (LIBSVM) data set. The number of training examples is increased in the experiment, and an SVM is trained in each step. The following performance characteristics are determined for each of the SVMs:
the classification error rate on the training set,
the classification error rate on the corresponding test set,
the number of support vectors,
the CPU execution time needed to train the linear SVM.
Figure 8.15. The classification error rate of the linear SVM on the training and the test sets against the number of training examples.
The first figure shows that the classification error on the training and test sets are roughly the same, independently of the number of training examples.
The second and the third figures show that both the number of support vectors and the CPU execution time increase linearly with the number of training examples.