College graduates employment prediction based on Principal Component Analysis and the combined adaptive boosting and the back propagation neural network algorithm

机译：基于主成分分析，自适应提升与反向传播神经网络算法相结合的高校毕业生就业预测

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摘要

In order to improve the prediction accuracy of the back propagation (BP) neural network model, a prediction model is presented based on the combined adaptive boosting (AdaBoost) and the back propagation neural network algorithm. The efficiency of the proposed prediction model is proved by predicting the college graduates employment. The computer simulations have shown that this model is effective and suitable. It has higher prediction accuracy and is applicable to practice.

机译：为了提高BP神经网络模型的预测精度，提出了一种基于组合自适应增强（AdaBoost）和BP神经网络算法的预测模型。通过预测大学毕业生的就业情况证明了所提预测模型的有效性。计算机仿真表明，该模型是有效且合适的。具有较高的预测精度，适用于实践。

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《International Conference on Natural Computation;International Conference on Fuzzy Systems and Knowledge Discovery》|2015年|1133-1137|共5页
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Xiangyang Liu; Juan Bao; Yan Jiang; Zhunping Ke; Changbo Wang;
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AdaBoost algorithm; BP neural network; prediction of the college graduates employment;

机译：AdaBoost算法; BP神经网络;大学生就业预测;

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4. College graduates employment prediction based on Principal Component Analysis and the combined adaptive boosting and the back propagation neural network algorithm [C] . Xiangyang Liu, Juan Bao, Yan Jiang, International Conference on Natural Computation . 2015

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College graduates employment prediction based on Principal Component Analysis and the combined adaptive boosting and the back propagation neural network algorithm

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