A General Theory of Supplementary Information Assignment and Analysis of Error Rates
Journal
Proceedings of MIRU 2008
Presentation number
IS1-9
Pages
pp.388-393
Reviewed
Not reviewed
Date
July 2008
Research fields
Pattern recognition
Key
Iwamura2008_MIRU (id=1023)
Abstract
Pattern recognition with supplementary information is a new
pattern recognition framework that determines an output class by
combining a feature vector extracted from the pattern and
supplementary information suggesting the true class.
Under the condition that supplementary information does not contain error,
a theory that reduces error rates
have been proposed.
However, in the real world, we cannot observe any measure without error.
Thus, in this paper, we discuss how to reduce error rates
using the erroneous supplementary information, and
confirm the effect experimentally using artificial samples
following the normal distribution with a common covariance matrix.
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