Affiliation:
1. Hebei Women’s Vocational College, Shijiazhuang, Hebei 050081, China
2. Hebei GEO University, Shijiazhuang, Hebei 050031, China
Abstract
According to the traditional classification method of network capital resources, there are some problems such as low efficiency, low recall rate, and low precision rate of information. Therefore, this paper proposes a new classification method of network capital resources based on SVM algorithm. Firstly, the original sample data are analyzed by principal component analysis to realize the design of resource classification process. Then, the dimension reduction of network resources data is realized by word segmentation and denoising. Thirdly, the reduced sample data are trained by the SVM classifier, and the best parameters of the reduced data are obtained by the grid search method. Lastly, the search range of SVM classifier parameters based on the original sample data is reset, so as to quickly obtain the best SVM classifier parameters of the original sample data and realize the classification. The experimental results show that this method can improve the recall and precision of network resource information and shorten the classification time of network resources.
Funder
Social Science Foundation of Hebei Province of China
Subject
Computer Networks and Communications,Information Systems
Cited by
1 articles.
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