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http://dx.doi.org/10.17703/IJACT.2017.5.2.82

Influence on overfitting and reliability due to change in training data  

Kim, Sung-Hyeock (Dept. of Medical IT Marketing, Eulji University)
Oh, Sang-Jin (Dept. of Medical IT Marketing, Eulji University)
Yoon, Geun-Young (Dept. of Medical IT Marketing, Eulji University)
Jung, Yong-Gyu (Dept. of Medical IT Marketing, Eulji University)
Kang, Min-Soo (Dept. of Medical IT Marketing, Eulji University)
Publication Information
International Journal of Advanced Culture Technology / v.5, no.2, 2017 , pp. 82-89 More about this Journal
Abstract
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 GradientDescentOptimize 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.
Keywords
Overfitting; machine learning; Deep-learning; cross-entropy; Tensorflow; Mnist dataset; Artificial Intelligence; Softmax regression; Reliability; loss-function;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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