構造適応型Deep Belief Network事前学習を考慮した知識獲得の検討

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  • Knowledge Acquisition in Consideration of Pre-training for Adaptive Structural Learning of Deep Belief Network

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Abstract?Deep Learning has a hierarchical network architecture to represent the complicated feature of in-put patterns. We have developed the adaptive structure learning method of Deep Belief Network (DBN) that can discover an optimal number of hidden neurons for given input data in a Restricted Boltzmann Machine (RBM)by neuron generation-annihilation algorithm, and hidden layers in DBN. The knowledge extraction method from the developed DBN and the recti?cation method of the signal ?ow on the wrong path have been developed. The ?ne-tuning method can reach an incredible high accuracy of classi?cation (the best record). In Deep Learning, the layer-wise unsupervised pre-training can construct abstract and concrete modes of information processing. In this paper we improve the knowledge acquisition method to adopt a distinction between abstract and concrete. The empirical study was executed on the ChestX-ray8 database.

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