2003 | OriginalPaper | Chapter
Back Propagation with Randomized Cost Function for Training Neural Networks
Authors : H. A. Babri, Y. Q. Chen, Kamran Ahsan
Published in: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
Publisher: Springer Berlin Heidelberg
Included in: Professional Book Archive
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A novel method to improve both the generalization and convergence performance of the back propagation algorithm (BP) by using multiple cost functions with a randomizing scheme is proposed in this paper. Under certain conditions, the randomized technique will converge to the global minimum with probability one. Experimental results on benchmark Encoder-Decoder problems and the NC2 classification problem show that the method is effective in enhancing BP’s convergence and generalization performance.