Application of Evolutionary Multiobjective Optimization in L1-Regularization of CNN
Misaki Kitahashi, Hisashi Handa · 2019
A convolutional neural network (CNN) is a type of multilayered neural network, which has a structure superior in handling images and natural languages. L1-regularization may be used as a means to prevent overfitting of CNN and improve generalization performance. However, it is difficult to set an appropriate coefficient of the regularization term. Therefore, in this research, we use MOEA/D to determine the value of hyper parameter. Experimental results on MNIST show the effectiveness of the proposed approach.