The use of recurrent neural networks for classification

Recurrent neural networks are widely used for context dependent pattern classification tasks such as speech recognition. The feedback in these networks is generally claimed to contribute to integrating the context of the input feature vector to be classified. This paper analyses the use of recurrent neural networks for such applications. We show that the contribution of the feedback connections is primarily a smoothing mechanism and that this is achieved by moving the class boundary of an equivalent feedforward network classifier. We also show that when the sigmoidal hidden nodes of the network operate close to saturation, switching from one class to the next is delayed, and within a class the network decisions are insensitive to the order of presentation of the input vectors.<<ETX>>