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dc.contributor.authorShimodaira, Hiroshi
dc.contributor.authorRokui, Jun
dc.contributor.authorNakai, Mitsuru
dc.coverage.spatial10en
dc.date.accessioned2006-05-15T12:02:46Z
dc.date.available2006-05-15T12:02:46Z
dc.date.issued1998-08
dc.identifier.citationSPR'98 2nd Int. Workshop on Statistical Techniques in Pattern Recognitionen
dc.identifier.urihttp://hdl.handle.net/1842/1060
dc.description.abstractA novel method to improve the generalization performance of the Minimum Classification Error (MCE) / Generalized Probabilistic Descent (GPD) learning is proposed. The MCE/GPD learning proposed by Juang and Katagiri in 1992 results in better recognition performance than the maximum-likelihood (ML) based learning in various areas of pattern recognition. Despite its superiority in recognition performance, as well as other learning algorithms, it still suffers from the problem of "over-fitting" to the training samples. In the present study, a regularization technique has been employed to the MCE learning to overcome this problem. Feed-forward neural networks are employed as a recognition platform to evaluate the recognition performance of the proposed method. Recognition experiments are conducted on several sorts of data sets.en
dc.format.extent159816 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherInternational Association for Pattern Recognitionen
dc.titleModifed Minimum Classification Error Learning and Its Application to Neural Networksen
dc.typeConference Paperen


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