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Takahiko Horiuchi, "Decision Rule for Pattern Classification by Integrating Interval Feature Values," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 4, pp. 440448, April, 1998.  
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@article{ 10.1109/34.677286, author = {Takahiko Horiuchi}, title = {Decision Rule for Pattern Classification by Integrating Interval Feature Values}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {20}, number = {4}, issn = {01628828}, year = {1998}, pages = {440448}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.677286}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Decision Rule for Pattern Classification by Integrating Interval Feature Values IS  4 SN  01628828 SP440 EP448 EPD  440448 A1  Takahiko Horiuchi, PY  1998 KW  Bayesian classification KW  DempsterShafer theory KW  integration KW  pattern classification KW  probability. VL  20 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Abstract—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 DempsterShafer 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.
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