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A multi-class classification with a probabilistic localized decoder

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2 Author(s)
Takenouchi, T. ; Nara Inst. of Sci. & Technol., Nara ; Ishii, S.

Based on the framework of error-correcting output coding (ECOC), we formerly proposed a multi-class classification method in which mis-classification of each binary classifier is regarded as a bit inversion error based on a probabilistic model of the noisy channel. In this article, we propose a modification of the method, based on localized likelihood, to deal with the discrepancy of metric between assumed by binary classifiers and underlying the dataset. Experiments using a synthetic dataset are performed, and we observe the improvement by the localized method.

Published in:

Signal Processing and Information Technology, 2007 IEEE International Symposium on

Date of Conference:

15-18 Dec. 2007