【摘要】 针对半监督分类模型存在的模型复杂度高、构造正则化项难度大的问题，从丰富样本特征表示的角度出发，构造了自适应图结构的融合网络模型（AGSH）。该模型在卷积神经网络模型（CNN）基础上引入了自适应图卷积神经网络（AGCN）提取CNN模型特征间的关系。对AGSH模型泛化性能的分析证明了该模型在解决半监督相关问题时的有效性。实验结果表明：融合模型在五种图像数据集上的分类精度相比于单一CNN模型分类精度均有提升。研究结论为解决小样本分类问题的建模方法提供了参考。更多还原

【Abstract】 To solve the problems of higher model complexity and difficulty in constructing regularization items in the semi-supervised classification model, a new semi-supervised image classification model named AGSH from the perspective of enriching sample feature representation is constructed, which is fusion with an adaptive graph structure. The model AGSH introduces the adaptive graph convolutional neural network AGCN, aiming to extract the relationship between the features of the CNN model based on the convolutional neural network model CNN.The analysis of the generalization performance of the AGSH model also shows the effectiveness of solving semi-supervised related problems. The experimental results show that the accuracy of the AGSH model is improved compared with that of the single CNN model on the five image datasets. The research expands the content of the semi-supervised image classification algorithm and provides an essential reference for the modeling method to solve the few-sample classification problem.更多还原