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<p><b>Abstract</b>—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. Therefore, it is necessary to construct more flexible and robust theory for pattern classification. 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, an integration algorithm is proposed, which integrates information observed by several information sources with considering source values.</p>
Bayesian classification, Dempster-Shafer theory, integration, pattern classification, probability.

T. Horiuchi, "Decision Rule for Pattern Classification by Integrating Interval Feature Values," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 20, no. , pp. 440-448, 1998.
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