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检索条件"主题词=Improved Kullback-Leibler divergence sparse autoencoder"
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Battle damage assessment based on an improved kullback-leibler divergence sparse autoencoder
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Frontiers of Information Technology & Electronic Engineering 2017年 第12期18卷 1991-2000页
作者: Zong-feng QI Qiao-qiao LIU Jun WANG Jian-xun LI State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Luoyang 471003 China MOE Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai 200240 China Luoyang Electronic Equipment Test Center of China Luoyang 471000 China
The nodes number of the hidden layer in a deep learning network is quite difficult to determine with traditional methods. To solve this problem, an improved kullback-leibler divergence sparse autoencoder (KL-SAE) is... 详细信息
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