Novel Approach to Improve the Performance of Artificial Neural Networks

Artificial neural networks, inspired by the information-processing strategies of the brain, are proving to be useful in a variety of the applications including object classification problems and many other areas of interest, can be updated continuously with new data to optimize its performance at any instant. The performance of the neural classifiers depends on many criteria i.e., structure of neural networks, initial weights, feature data, number of training samples used which are all still a challenging issues among the research community. This paper discusses a novel approach to improve the performance of neural classifier by changing the methodology of presenting the training samples to the neural classifier. The results are proving that network also depends on the methodology of giving the samples to the classifier. This work is carried out using real world dataset