rejection, adaptive learning.

[wei li]

Hi, we are doing text classification using a feedforword neural network.
Through our experiments, we found two problems: 
1) in our class definition, we have texts which are not belong to
    any classes. We threshold the output, if the output is below
    the threshold, the input is considered as rejected. It did not
     seem to work well for the patterns which sould be rejected.
2) When some patterns can not be correctly recognized, we have to retrain the
   system including these new patterns. We wonder if there is way to 
   gradually adapt the system without having to retrain the old correctly 
   learned patterns too. We have tried RCE network for adaptive learning too,
   but it seems that if we do not retraining the old patterns, some previously
   correctly learned patterns will become wrong.
Any comments on approaches that could reject patterns and adapt to new patterns?

Wei Li
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