Earticle

초록

영어

A common problem with neural network learning is that it is too suitable for the specificity of learning. In this paper, various methods were compared to avoid overfitting: regularization, drop-out, different numbers of data and different types of neural networks. Comparative studies of the above-mentioned methods have been provided to evaluate the test accuracy. I found that the more data using method is better than the regularization and dropout methods. Moreover, we know that deep convolutional neural networks outperform multi-layer neural networks and simple convolution neural networks.

목차

Abstract
1. Introduction
2. Over-fitting in Supervised Training
3. Methods to avoid neural network overfitting
3.1 Use Regularization
3.2 Use Dropout
3.3 Use Different number of data
3.4 Use Different types of Neural Network
4. Empirical Results and Observation
5. Conclusion
Acknowledgement
References

키워드

저자

• Ho-Chan Kim [ Professor, Department of Electrical Engineering, Jeju National University, Korea ]
• Min-Jae Kang [ Professor, Department of Electronic Engineering, Jeju National University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380