LAUSR

Modern neural network architectures are powerful models. They have been proven efficient in many fields, such as imaging and acoustic. However, these neural networks involve a long-running and time-consuming process. To accelerate the training process, we propose a two-stage approach based on data analysis and focus on the gravity center concept. The neural network is first trained on reduced data represented by a set of centroids of the original data points, and then the learned weights are used to initialize a second training phase of the neural network over the full-blown data. The design of deep neural networks is extremely difficult, and the primary objective is to achieve high performance. In this study, we apply the Taguchi method to select good values for the factors required to build the proposed architecture.