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Fourth International Conference Document Analysis and Recognition (ICDAR'97)
Pattern Classification Method by Integrating Interval Feature Values
Ulm, GERMANY
August 18-August 20
ISBN: 0-8186-7898-4
Takahiko Horiuchi, Institute of Information Sciences and Electronics University of Tsukuba
Pattern classification based on Bayesian statistical decision theory needs a complete knowledge of the probability laws to perform the classification. In the actual pattern classification, however, it is generally impossible to get the complete knowledge as constant feature values by the influence of noise. In this paper, a pattern classification theory using feature values defined on closed interval is formalized in the framework of Dempster-Shafer measure. Then, in order to make up lacked information, a new integration algorithm is proposed.
Citation:
Takahiko Horiuchi, "Pattern Classification Method by Integrating Interval Feature Values," icdar, pp.847, Fourth International Conference Document Analysis and Recognition (ICDAR'97), 1997
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