一种基于胶质细胞链的改进深度信念网络模型

深度信念网络(Deep belief network, DBN) 是一种从无标签数据学习特征的多层结构模型. 在同一层单元间缺少连接, 导致数据中的深度关联特征难以提取. 受到人脑中胶质神经细胞机制的启示, 提出一种基于胶质细胞链的改进 DBN 模型及其学习算法, 以提取更多数据信息. 在标准图像分类数据集上的实验结果表明, 与其他几种模型相比, 本文提出的改进 DBN 模型可以提取更为优秀的图像特征, 提高分类准确率.

An Improved Deep Belief Network Inspired by Glia Chains

Deep belief network (DBN) is a hierarchical model for learning feature representations from unlabeled data. However, there are no interconnections among the neural units in the same layer and the mutual information of different neural units may be ignored. Inspired by functions of glia cells in the neural network structure of human brain, we propose a variant structure of DBN and an improved learning algorithm to extract more information of the data. Experimental results based on benchmark image datasets have shown that the proposed DBN model can acquire better features and achieve lower error rates than a traditional DBN and other compared learning algorithms.