Feature Extraction of Network Small Sample Data Based on Convolutional Neural Network
In response to the problems of high false extraction rate and low credibility in current data feature extraction methods,this study applies a new network small sample data feature extraction method designed based on convolutional neural networks.By mapping small sample data in the Hilbert space and adjusting the relative entropy of the data,balanced processing of the data is achieved.Genera-tive adversarial networks are used to expand and correct the small sample data,and convolutional neural networks are used to extract features from the expanded data,achieving feature extraction of network small sample data based on convolutional neural networks.Experimental results have shown that the er-ror rate of the design method does not exceed 1％,and the reliability is above 0.9,which can achieve ac-curate feature extraction of small sample data in the network.
万方数据
维普
