A Survey of Regularization Methods for Deep Neural Network

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.

Provided by: International Journal of Computer Science and Mobile Computing (IJCSMC) Topic: Networking Date Added: Nov 2014 Format: PDF

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