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- W2354365887 abstract "Study tax forecasting.Because the tax data are of high nonlinear and redundancy,the traditional tax forecasting method has low precision.We proposed a tax forecasting model based on principal component analysis of support vector machine forecasting model(PCA-SVM).Firstly,PCA is used for variable selection in order to reduce the indicator's redundancy information and the input data of SVM,and improve the speed of the support vector machines.Then,the hybrid forecasting model is used to test and analyze the tax data in china for 2001 to 2004.The empirical results reveal that compared with the reference models,the PCA-SVM has great superiority in predicting accuracy and high generalization ability,and is a kind of effective tax forecasting model." @default.
- W2354365887 created "2016-06-24" @default.
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- W2354365887 date "2011-01-01" @default.
- W2354365887 modified "2023-09-23" @default.
- W2354365887 title "Studyon Tax Forecasting Base on Principal Component Analysis and Support Vector Machines" @default.
- W2354365887 hasPublicationYear "2011" @default.
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