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- W2356389635 abstract "Unemployment early-warning system modeling is a special and complex problem with small sample,high dimension,nonlinearity,and noise data.The theory,method,and procedure on modeling unemployment early-warning systems based on regression analysis are mainly discussed.Firstly,the pre-processing methods are given,including missing data processing,data scaling,and dimension reducing.Five regression techniques are further analyzed: least square regression,Logistic regression,ridge regression,BP neural network,and support vector regression.Experimental results based on social and economical investigation data from Guangdong Province show that,support vector regression outperforms the other four methods,logistic regression is worst,and the forecast accuracies of least square regression,ridge regression,and BP neural network are between those of support vector regression and logistic regression." @default.
- W2356389635 created "2016-06-24" @default.
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- W2356389635 date "2012-01-01" @default.
- W2356389635 modified "2023-09-25" @default.
- W2356389635 title "Empirical Research on Unemployment Early-Warning Based on Regression Analysis" @default.
- W2356389635 hasPublicationYear "2012" @default.
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