| 基于SVM的人才资源对陕西经济增长影响的预测模型研究 |
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| 引用本文： | 李朋林,赵晶,马琰玲.基于SVM的人才资源对陕西经济增长影响的预测模型研究[J].西安科技大学学报,2012,32(5):637-642. |
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| 作者姓名： | 李朋林 赵晶 马琰玲 |
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| 作者单位： | 1. 西安科技大学管理学院,陕西西安,710054
2. 长庆油田第五采油厂,710021 |
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| 摘 要： | 在构建陕西省人才资源指标体系的基础上,运用SVM方法构建了陕西省人才资源与经济增长的关系模型,并与基于BP神经网络的模型进行了对比分析。结论表明:采用结构风险最小化准则的SVM回归方法比BP神经网络模型具有更高的预测精度,是经济增长预测研究中的一种新型、有效的方法。
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| 关 键 词： | 经济增长 人才资源 支持向量机 BP神经网络 |
Forecast model of talent resources to economic growth in Shaanxi province based on SVM |
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| LI Peng-lin
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ZHAO Jing
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MA Yan-ling.Forecast model of talent resources to economic growth in Shaanxi province based on SVM[J].JOurnal of XI’an University of Science and Technology,2012,32(5):637-642. |
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| Authors: | LI Peng-lin ZHAO Jing MA Yan-ling |
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| Institution: | ( College of Management,Xi＇ an University of Science and Technology, Xi＇ an 710054, China) |
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| Abstract: | Based on the index system of Shaanxi talent resources constructed in this paper, the relationship model between Shaanxi talent resources and economic growth is built using the method of Support Vector Machine. After that, the authors compare the predictive ability of the SVM model with BP neural network model. The results indicate that the SVM neural network model and therefore it is a new and growth. model has more precise predictive ability than BP effective method in predictive research of economic |
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| Keywords: | economic growth talent resources support vector machine BP neural network |
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