A Survey of Regularization Methods for Deep Neural Network?Journal: International Journal of Computer Science and Mobile Computing - IJCSMC (Vol.3, No. 11)
Publication Date: 2014-11-30
Authors : Nishtha Tripathi; Avani Jadeja;
Page : 429-436
Keywords : Deep neural networks; Regularization; Overfitting; Supervised learning learning; Dropout;
Mimicking the human psyche has been a core challenge in machine learning research. Deep Neural network inspired from the human Visual cortex system are powerful computational model represents the large features in a hierarchical way. Overfitting is a major problem in deep learning due to the presence of a large number of features. Dropout is a proficient and simple method to prevent co-adaptation of features and thus stymie to over fit. It simply drops hidden units with probability 0.5.Maxout a new activation unit built on dropout has improved accuracy on datasets. Other recent companions are DropConect, DropAll and stochastic pooling. Dropout achieved state-of-art results on many labelled benchmark datasets: MNIST, CIFAR-10, CIFAR-100 and SVHN. This paper reviews different techniques to reduce Overfitting in Neural Network.
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Last modified: 2014-11-24 22:18:09