Pattern Classification Method by Integrating Interval Feature Values

Ulm, GERMANY August 18-August 20

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http://doi.ieeecomputersociety.org/10.1109/ICDAR.1997.620631

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	ASCII Text	x
	Takahiko Horiuchi, "Pattern Classification Method by Integrating Interval Feature Values," Document Analysis and Recognition, International Conference on, pp. 847, Fourth International Conference Document Analysis and Recognition (ICDAR'97), 1997.

	BibTex	x
	@article{ 10.1109/ICDAR.1997.620631, author = {Takahiko Horiuchi}, title = {Pattern Classification Method by Integrating Interval Feature Values}, journal ={Document Analysis and Recognition, International Conference on}, volume = {0}, year = {1997}, isbn = {0-8186-7898-4}, pages = {847}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDAR.1997.620631}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Document Analysis and Recognition, International Conference on TI - Pattern Classification Method by Integrating Interval Feature Values SN - 0-8186-7898-4 SP EP A1 - Takahiko Horiuchi, PY - 1997 VL - 0 JA - Document Analysis and Recognition, International Conference on ER -

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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