Japanese / English

Detail of Publication

Text Language Japanese
Authors Masakazu Iwamura, Yoshio Furuya, Koichi Kise, Shinichiro Omachi, and Seiichi Uchida
Title 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 or not Not reviewed
Month & Year July 2008
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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