
훈련 데이터 개수와 훈련 횟수에 따른 과도학습과 신뢰도 분석에 대한 연구
A Study on Reliability Analysis According to the Number of Training Data and the Number of Training
- 김성혁(Sung Hyeock Kim) 오상진(Sang Jin Oh) 윤근영(Geun Young Yoon) 김완기(Wan Ki Kim)
- 한국인공지능학회
- 인공지능연구
- Vol.5 No. 1
- 등재여부 : KCI등재후보
- 2017.06
- 29 - 37 (9 pages)
The range of problems that can be handled by the activation of big data and the development of hardware has been rapidly expanded and machine learning such as deep learning has become a very versatile technology. In this paper, mnist data set is used as experimental data, and the Cross Entropy function is used as a loss model for evaluating the efficiency of machine learning, and the value of the loss function in the steepest descent method is We applied the Gradient Descent Optimize algorithm to minimize and updated weight and bias via backpropagation. In this way we analyze optimal reliability value corresponding to the number of exercises and optimal reliability value without overfitting. And comparing the overfitting time according to the number of data changes based on the number of training times, when the training frequency was 1110 times, we obtained the result of 92%, which is the optimal reliability value without overfitting.
1. 서론
2. 관련연구
3. 실험 및 결과
4. Conclusion
References