Sciweavers

Share
SSPR
1998
Springer

Modified Minimum Classification Error Learning and Its Application to Neural Networks

10 years 8 months ago
Modified Minimum Classification Error Learning and Its Application to Neural Networks
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, 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.
Hiroshi Shimodaira, Jun Rokui, Mitsuru Nakai
Added 06 Aug 2010
Updated 06 Aug 2010
Type Conference
Year 1998
Where SSPR
Authors Hiroshi Shimodaira, Jun Rokui, Mitsuru Nakai
Comments (0)
books