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Searching influence variables as well as forecasting the flowing of graduate employment is an ongoing activity of considerable significance. But the forecasting is complex due to the time series and complex factor inputs. The neural network method has been successfully employed to solve the multi factors problem. However the forecasting result is not ideal due to the nonlinearity and noise. In this...
It is hard to search the influence variables and to forecast the flowing of graduate employment due to the time series and complex factor inputs. Recently the neural network method has been successfully employed to solve the problem. However the forecasting result is not ideal due to the nonlinearity and noise. In this work, by combining Recurrent Neural Network (RNN) with Kernel Principal Component...
A two-stage neural network architecture constructed by combining recurrent neural network (RNN) with kernel feature extraction is proposed for stock prices forecasting. In the first stage, kernel independent component analysis (KICA) and kernel principal component analysis (KPCA) are used as feature extraction. In the second stage, RNN with kernel feature extraction is used to regression estimation...
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