5th International Conference on Spoken Language Processing

Sydney, Australia
November 30 - December 4, 1998

Improving The Generalization Performance Of The MCE/GPD Learning

Hiroshi Shimodaira, Jun Rokui, Mitsuru Nakai

Japan Advanced Institute of Science and Technology, Japan

A 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

Full Paper

Bibliographic reference.  Shimodaira, Hiroshi / Rokui, Jun / Nakai, Mitsuru (1998): "Improving the generalization performance of the MCE/GPD learning", In ICSLP-1998, paper 0795.