Partial and Random Updating Weights in Error Back Propagation Algorithm
Nasim Latifi, Ali Amiri
Journal of Networking Technology, Vol. 3, No. 1, 2012.
DOI: 
Link: http://www.dline.info/jnt/fulltext/v3n1/4.pdf
Abstract:
Multi-Layered Perceptron (MLP) is a useful supervised neural network for data classification. Error Back Propagation (EBP) algorithm is the common technique for training MLP. Standard EBP algorithm has challenges for largescale and heterogeneous data such as lack of memory and lowâ€“speed convergence, besides, computational load is high. In this paper, to overcome these drawbacks, a modified version of EBP has been proposed. It decreases the time and space complexity, and somewhat increases convergence speed of the standard EPB by partial and random updating of some of weights instead of all of them. Result of experiments on two standard dataset, confirms the effectiveness of the proposed algorithm.