Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-05-10 Cooperative journals: 《计算机应用研究》
Abstract： In order to improve the accuracy of text classification and solve the problem of insufficient utilization of node features by text graph convolution neural network, this paper proposes a new text classification model, which integrates the advantages of text graph convolution and Stacking integrated learning method. The model first learns the global expression of documents and words and the grammatical structure information of documents through text graph convolution neural network, and then secondary learns the features extracted by text graph convolution through integrated learning, so as to make up for the insufficient utilization of text graph convolution node features, and improve the accuracy of single label text classification and the generalization ability of the whole model. In order to reduce the time consumption of ensemble learning, the fusion algorithm removes the k-fold cross verification mechanism in ensemble learning. The fusion algorithm realizes the correlation between text graph convolution and stacking integrated learning method. The classification effect on R8, R52, Mr, Ohsumed, 20ng and other data sets is improved by more than 1.5%, 2.5%, 11%, 12% and 7% respectively compared with the traditional classification model. This method performs well in the comparison of classification algorithms in the same field.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-01-28 Cooperative journals: 《计算机应用研究》
Abstract： Due to the complexity and variety of glyphs, offline handwritten Chinese character recognition has always been a difficult problem of pattern recognition. The development of deep convolutional neural networks provides a direct and effective solution to this problem. This paper studied offline handwritten Chinese character recognition based on Inceptions neural network.It proposed an improved Inception structure, which took the advantages of simpler structure, easier network depth expansion and less training parameters. The method used the proposed structure to verifiy on dataset CISIA-HWDB1.1. The model achieved an average accuracy of 96.95%, by using stochastic gradient descent optimization algorithm. Experimental result shows that the improved Inception structure has better generalization performance and robustness in image classification, and can be easily extended to other applications.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-08-13 Cooperative journals: 《计算机应用研究》
Abstract： discovering user interest degree in massive data is an important way to implement information push. This paper proposes a page interest prediction algorithm based on singular value decomposition and collaborative filtering. The algorithm can extract the "false score" in the dominant user score data, and find user page interest and influence factors. The results of MATLAB simulation show that the collaborative filtering algorithm based on singular value decomposition is accurate and efficient in predicting the interest degree of user pages under the unavoidable situation of massive data sparsity.
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