In this experiment RBF kernel SVMs are trained on the
Ionosphere data set while the value of the
gamma of the RBF kernel and also
the value of the parameter
C are changed.
The average classification error rate from 10-fold cross-validation
is determined for each SVM. As a result, the values yielding the
best average classification error rate will be returned. The
following parameter values will be considered for
gamma: C = 2^n,
where -5 <= n <= 6,
gamma = 2^m, where
-10 <= m <= 4. Thus, the total
number of parameter value combinations considered is 180.
Ionosphere [UCI MLR]
Figure 8.23. The classification accuracy of the RBF kernel SVM trained on the entire data set using the optimal parameter values.
The first figure shows that the best average classification error rate
is achieved when the value of the parameter
C is 16
and the value of the parameter
0.015625. Note that these parameter values can not be considered as
the global optimum of the average classification error rate, since they
were obtained by performing a grid search that examines only a few points
of the search space.
The second figure shows that the RBF kernel SVM trained on the entire data set using the optimal parameter values performs very well.