Early Stopping Criteria for Levenberg-Marquardt Based Neural Network Training Optimization

  • Authors

    • Azizah Suliman
    • Batyrkhan Omarov
    2018-12-09
    https://doi.org/10.14419/ijet.v7i4.36.25382
  • Early Stop Condition, Levenberg-Marquardt Method, Neural Network, Overtraining.

  • Abstract

    In this research we train a direct distributed neural network using Levenberg-Marquardt algorithm. In order to prevent overtraining, we proposed correctly recognized image percentage based on early stop condition and conduct the experiments with different stop thresholds for image classification problem. Experiment results show that the best early stop condition is 93% and other increase in stop threshold can lead to decrease in the quality of the neural network. The correct choice of early stop condition can prevent overtraining which led to the training of a neural network with considerable number of hidden neurons.

     

     

  • References

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  • How to Cite

    Suliman, A., & Omarov, B. (2018). Early Stopping Criteria for Levenberg-Marquardt Based Neural Network Training Optimization. International Journal of Engineering & Technology, 7(4.36), 1194-1198. https://doi.org/10.14419/ijet.v7i4.36.25382

    Received date: 2019-01-04

    Accepted date: 2019-01-04

    Published date: 2018-12-09