Segmentation of heterogeneous document images : an ... - Tel

tel-00912566, version 1 - 2 Dec 2013
Figure 4.16: This figure shows the evolution of training error rate with 5 different
learning rates. All other parameters remain the same.
obtained error in each iteration. Any other parameter of the training remains
the same unless if stated otherwise.
Learning rate
Learning rate refers to the learning rate of the voted perceptron training algorithm.
It controls the fraction of computed deltas in each iteration of the
training that are contributing to the weights of the feature functions. The number
of ICM cycles is 10. The block overlap ratio is 0.25 and the width and height
of each block is half of the mean width and height of all text characters of the
page, respectively.
Figure 4.16 shows the evolution of training error with 5 different learning
rates. It suggests that a high value for learning rate forces the training to go into
a cycle. On the other hand, the training process is smooth with a small value
for learning rate (0.1 in this case) however the process converges to a higher
training error. In this particular experiment, a learning rate of 0.25 can be considered
as good because the number of misclassified sites decreases reasonably
fast without going into cycles.
Unfortunately, there are no rules of thumb to determine a best value for
learning rate without considering the number of blocks, number of features
functions and their characteristics.
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