We compared both versions of windowing on a variety of noise-free
domains. In each domain we ran a series of experiments with varying
training set sizes. For each training set size, 10 different subsets
of this size were selected from the entire set of preclassified
examples. All three algorithms, DOS, WIN-DOS-3.1, and
WIN-DOS-95 were run on each of these subsets and the results of the
10 experiments were averaged. For each experiment we measured the
accuracy of the learned theory on the entire example set and the total
run-time of the algorithm.^{8} To have a more reliable complexity measure than the
implementation-dependent run-time, we also measured the total number
of examples that were processed by the basic learning algorithm. For
DOS, this number is identical to the size of the respective training
set, while for the windowing algorithms it is computed as the sum of
the training set sizes of all iterations of windowing. For the
noise-free case, it turned out that this factor determined the
run-time of the algorithms, so that its graphs were almost identical
to the graphs for run-time results. Therefore, for reasons of space
efficiency, we will only present the run-time curves (except in
Figure 4).^{9}

All experiments shown below were conducted with a setting of *InitSize* = 100 and *MaxIncSize* = 50. These settings are
briefly discussed in section 4.6.