In this paper, we introduce a new model in which the classifier
receives not only a pattern itself but also supplementary information
that assists recognition.
This model can greatly improve recognition performance with a few bits
of information.
For practical use of pattern recognition, rejection is one of the most
important operations.
The proposed model also can reduce error rates by rejection.
How to assign such information is described, and
possible recognition rates are estimated.
The proposed model will enable us to achieve
an arbitrary recognition rate with an arbitrary rejection rate
with certain bits of supplementary information required.
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