TY  - JOUR
TI  - Effect on model performance of regularization methods
AB  - Artificial Neural Networks with numerous parameters are tremendously powerful machine learning systems. Nonetheless, overfitting is a crucial problem in such networks. Maximizing the model accuracy and minimizing the amount of loss is significant in reducing in-class differences and maintaining sensitivity to these differences. In this study, the effects of overfitting for different model architectures with the Wine dataset were investigated by Dropout, AlfaDropout, GausianDropout, Batch normalization, Layer normalization, Activity normalization, L1 and L2 regularization methods and the change in loss function the combination with these methods. Combinations that performed well were examined on different datasets using the same model. The binary cross-entropy loss function was used as a performance measurement metric. According to the results, the Layer and Activity regularization combination showed better training and testing performance compared to other combinations.
AU  - ASKER, Mehmet Emin
AU  - budak, cafer
AU  - MENÇİK, VASFİYE
DO  - 10.24012/dumf.1051352
PY  - 2021
JO  - Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi
VL  - 12
IS  - 5
SN  - 1309-8640
SP  - 757
EP  - 765
DB  - TRDizin
UR  - http://search/yayin/detay/498862
ER  -