Abstract
A 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, 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 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 datasets.
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Keywords
- Discriminant Function
- Recognition Performance
- Dynamic Time Warping
- Generalization Performance
- Tikhonov Regularizer
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Shimodaira, H., Rokui, J., Nakai, M. (1998). Modified minimum classification error learning and its application to neural networks. In: Amin, A., Dori, D., Pudil, P., Freeman, H. (eds) Advances in Pattern Recognition. SSPR /SPR 1998. Lecture Notes in Computer Science, vol 1451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033303
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DOI: https://doi.org/10.1007/BFb0033303
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