backprop for classification

[xiru@Think.COM]

While we trained a standard backprop network for some classification task
(one output unit for each class), we found that when the classes are not
evenly distribed in the training set, e.g., 50% of the training data belong
to one class, 10% belong to another, ... etc., then the network always biased
towards the classes that have the higher percentage in the training set.
Thus, we had to post-process the output of the network, giving more weights
to the classes that occur less frequently (in reverse proportion to their
population). 

I wonder if other people have encountered the same problem, and if  there
are better ways to deal with this problem.

Thanks in advance for any replies.


- Xiru Zhang

Thinking Machines Corp.