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=item B<show_class_labels_for_misclassified_samples_in_stage( $stage_index ):>

As with the previous method, this method is useful mostly for debugging. It returns
class labels for the samples misclassified by the stage whose integer index is
supplied as an argument to the method.  Say you have 10 stages in your cascade.  The
value of the argument C<stage_index> would go from 0 to 9, with 0 corresponding to
the base tree.

=item B<trust_weighted_majority_vote_classifier():>

Uses the "final classifier" formula of the AdaBoost algorithm to pool together the
classification decisions made by the individual trees while taking into account the
trust factors associated with the trees.  As mentioned earlier, we associate with
each tree of the cascade a trust factor that depends on the overall misclassification
rate associated with that tree.

=back

See the example scripts in the C<ExamplesBoosting> subdirectory for how to call the
methods listed above for classifying individual data samples with boosting and for
bulk classification when you place all your test samples in a single file.