The process demonstrates the influence of the parameter
C on performance of the linear SVM. Linear
SVMs are trained on a subset of the Wine data
set while the value of the parameter
increased from 0.001 to 100. The classification error rate on the
training set and also the number of support vectors are determined
for each SVM.
A subset of the Wine data set [UCI MLR].
Figure 8.9. A subset of the Wine data set used in the experiment (2 of the total of 3 classes and 2 of the total of 13 attributes was selected). Note that the classes are not linearly separable.
The first figure shows that the classification error rate
quickly falls below 6% as the value of the parameter
C is increased and then it remains
The second figure shows that the number of support vectors decreases
similarly with the increasing value of the parameter
C, although not so rapidly as the
classification error rate.