PDFAbstract: This paper extends the earlier work on an oscillating error correction technique. Specifically, it extends the design to include further corrections, by adding new layers to the classifier through a branching method. This technique is still consistent with earlier work and also neural networks in general. With this extended design, the classifier can now achieve the high levels of accuracy reported previously
Keywords: classifier, oscillating error, neural network, multiple layers, branching, cellular automata REFERENCES [1] Akkar, H.A. and Mahdi, F.R., 2016. Evolutionary algorithms for neural networks binary and real data classification. Int J Sci Technol Res, 5(7), pp.55-60. [2] Asim, A., Li, Y., Xie, Y. and Zhu, Y. (2002). Data Mining For Abalone, Computer Science 4TF3 Project, Supervised by Dr. Jiming Peng, Department of Computing and Software, McMaster University, Hamilton, Ontario. [3] Dershowitz, N. and Falkovich, E. (2015). Cellular Automata are Generic, U. Dal Lago and R. Harmer (Eds.): Developments in Computational Models 2014 (DCM 2014). EPTCS 179, pp. 17-32,
Cite this paper
Kieran Greer. (2018) An Improved Oscillating-Error Classifier with Branching. International Journal of Computers, 3 , 38-43
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