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The problem of over fitting in DBN is extensively focused on since different networks may respond differently to an unknown input. In this study, a regularization-reinforced deep belief network (RrDBN) is proposed to improve generalization ability. In RrDBN, a special regularization-reinforced term is developed to make the weights in the unsupervised training process to attain a minimum magnitude...
Restricted Boltzmann Machine is a fundamental method in deep learning networks. Training and generalization is an ill-defined problem in that many different networks may achieve the training goal; however each will respond differently to an unknown input. Traditional approaches include stopping the training early and/or restricting the size of the network These approaches ameliorate the problem of...
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