
can reduce the testing error. On the other hand, the variances of the
classification are relatively high.
Rejecting samples when classifying such kind of data turns out to
be a sound approach leading to more robust results, especially when
the distribution of the classes in the data is heavily overlapping. In
future work, it could be promising to implement an iterative classifier
training procedure, where the training data can be rejected.
The results presented in Tables 1 to 3 are preliminary and must
be further evaluated in several directions:
1. Feature extraction techniques as described in the previous
sections have been successfully applied ...