MMI-based training for a probabilistic neural network

Probabilistic neural networks (PNNs) that incorporate the Bayesian decision rule and statistical models have been widely used for pattern classification. Efficient estimation of the PNN's weights, however, is still a major problem. In this paper, we propose a new training scheme based on a discriminative criterion, maximum mutual information (MMI), and apply this method to the log-linearized Gaussian mixture network (LLGMN) which is one of the PNNs. The MMI training achieves a consistent estimator of network weights, and includes the conventional maximum likelihood (ML) algorithm as a special case. Also, the dynamics of terminal attractor (TA) is introduced for iteration control of the MMI training. Finally, the classification ability of the proposed method is examined with a pattern classification problem of the electromyogram (EMG) signals, and found that the MMI training results in better classification than the conventional ML algorithm.