改进的稀疏深度置信网络

深度学习作为近年热门研究领域，具有极大的应用前景，但存在过拟合、欠拟合、隐藏层数和节点数选取等诸多问题。针对深度置信网络存在的过拟合问题，借鉴压缩感知理论和零范数的数学性质，构建了一种基于无均值高斯分布函数的稀疏深度置信网络。通过在预训练阶段添加稀疏正则项，进一步改进深度置信网络训练过程的方法加以解决过拟合问题。利用ORL和MINIST两种公开数据集上对该改进方案进行验证分析，结果表明其比现有的改进方案在稀疏性和准确性上有较大提升。

Improved Sparse Deep Belief Network

As a popular research field in recent years,deep learning has great application prospects,but there are many problems such as over-fitting,under-fitting,hidden layer number and node number selection.Aiming at the over-fitting problem of deep belief network,based on the theory of compressed sensing and the mathematical properties of zero norm,a sparse deep belief network based on non-means Gaussian distribution function is constructed.The over-fitting problem is solved by adding a sparse regular term in the pre-training phase,further to improve the deep belief network training process.Using the ORL and MINIST two public data sets to verify and analyze the improved scheme,the results show that it has a greater improvement in sparsity and accuracy than the existing improved schemes.