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.

• Type: Conference
• Year: 1998
• Where: SSPR
• Authors: Hiroshi Shimodaira, Jun Rokui, Mitsuru Nakai
• Keywords: Better Recognition Performance, Minimum Classification Error, Pattern Recognition, Recognition Performance, SSPR 1998