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dc.contributor.authorShimodaira, Hiroshi
dc.contributor.authorRokui, Jun
dc.contributor.authorNakai, Mitsuru
dc.coverage.spatial4en
dc.date.accessioned2006-05-11T16:52:07Z
dc.date.available2006-05-11T16:52:07Z
dc.date.issued1998-12
dc.identifier.citationIn ICSLP-1998, paper 0795.en
dc.identifier.urihttp://www.isca-speech.org/archive/icslp_1998/index.html
dc.identifier.urihttp://hdl.handle.net/1842/1009
dc.description.abstractA novel method to prevent the over-fitting effect and improve the generalization performance of the Minimum Classification Error (MCE) / Generalized Probabilistic Descent (GPD) learning is proposed. The MCE/GPD method, which is one of the newest discriminative-learning approaches proposed by Katagiri and Juang in 1992, results in better recognition performance in various areas of pattern recognition than the maximum-likelihood (ML) based approach where a posteriori probabilities are estimated. Despite its superiority in recognition performance, it still suffers from the problem of over-fitting to the training samples as it is with other learning algorithms. In the present study, a regularization technique is employed to the MCE method 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 datasets. The proposed method shows better generalization performance than the original one.en
dc.format.extent73475 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherInternational Speech Communication Associationen
dc.titleImproving The Generalization Performance Of The MCE/GPD Learningen
dc.typeConference Paperen


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