Conscience algorithm in neural network

A type of network called the Contender Network (CN) was earlier proposed by Ng, Erdogan and Ng (1995). A classification algorithm is used to assign weighted vote in a monotonically decreasing function of the rank in CN. Modification to the CN classification algorithm known as the conscience algorithm is presented. However, a new problem is encountered when the conscience algorithm is used in CN. We name this problem as saturation problem (i.e. when saturation stage of the neural network is reached). This saturation problem is solved by introducing a count threshold. The threshold is decided rigorously through many experiments based on the criteria of the accuracy, error and confusion rates of the network performance. We present experiments that show that conscience algorithm introduced during training with appropriate count threshold can improve the network performance. Experimental results of this approach are presented and discussed through the application of the neural network in digit classification.