A maximum likelihood neural network based on a log-linearized Gaussian mixture model

Abstract

The present paper proposes a new probabilistic neural network based on a log-linearized Gaussian mixture model, which can estimate a posteriori probability for pattern classification problems. Although a structure of the proposed network represents a statistic model, a forward calculation and a backward learning rule based on the maximum likelihood estimation can be defined in the same manner as the error back propagation neural network model. It is shown from experiments that considerably high classification performance for small sample size of training data can be realized and a structure of the network is easily determined by an incorporated statistical model.