Probabilistic feed-forward neural network
Bharathi B. Devi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
In this paper, we propose a Gaussian neuron model for feedforward type of neural networks and a method to adapt the above network for any input, not necessarily in the range [0,1]. An error function based on the class label and a priori probability is defined and gradient descent procedure, with backpropagating error, is used for finding the optimal set of parameters of this network. Different approaches are proposed for increasing the rate of convergence of this network. Experimental results are given for continuous data from speech waveform and XOR type of data.