本研究应用阅读获得的SVR模型(Si mple View of Reading),探讨单词解码、英语语言理解和一般认知能力在汉语儿童英语阅读学习中的作用及其影响途径。92名小学四年级学生完成了英语阅读理解、英语假词拼读、英语语言理解、汉语语言理解...本研究应用阅读获得的SVR模型(Si mple View of Reading),探讨单词解码、英语语言理解和一般认知能力在汉语儿童英语阅读学习中的作用及其影响途径。92名小学四年级学生完成了英语阅读理解、英语假词拼读、英语语言理解、汉语语言理解和一般认知能力测验,其中47名学生还完成了英语单词认读测验。相关和回归分析结果表明,控制一般认知能力后,单词解码和英语语言理解均能显著地解释英语阅读理解;进一步控制汉语语言理解后,英语语言理解对英语阅读理解仍具有显著的独立贡献。单词认读和英语语言理解对于英语阅读理解存在显著的交互作用。一般认知能力对英语阅读理解没有显著的直接作用,而以假词拼读能力为中介间接影响英语阅读理解。展开更多
为提高民航客运量预测精准度,本文针对近18年的时间序列民航客运量数据,构建极限梯度提升树XGBoost预测模型,进行多特征分析,处理季节、节假日等主要因素,并与SVR模型进行对比。通过对比预测曲线图,反映出SVR模型在高维空间中可以找到...为提高民航客运量预测精准度,本文针对近18年的时间序列民航客运量数据,构建极限梯度提升树XGBoost预测模型,进行多特征分析,处理季节、节假日等主要因素,并与SVR模型进行对比。通过对比预测曲线图,反映出SVR模型在高维空间中可以找到最优超平面来拟合数据,XGBoost模型适用于复杂的非线性关系建模。实验结果表明,XGBoost预测模型相比于SVR向量回归模型、线性模型与随机森林模型,其精准度更高且对影响因素敏感;XGBoost模型有更高的R2和更低的MSE,能够更有效提高民航客运量的预测精度和预测稳定性,为制定航空运输生产计划和发展航空运输业提供了重要参考。In order to improve the accuracy of civil aviation passenger traffic prediction, this paper, based on the civil aviation passenger traffic data of recent 18 years, builds the ultimate gradient lift tree XGBoost prediction model, conducts multi-feature analysis, processes major factors such as seasons and holidays, and compares it with the SVR model. By comparing the prediction curves, it shows that SVR model can find the optimal hyperplane to fit the data in the high-dimensional space, and XGBoost model is suitable for complex nonlinear relationship modeling. The experimental results show that compared with SVR vector regression model, linear model and random forest model, XGBoost prediction model is more accurate and sensitive to influencing factors. XGBoost model has higher R2 and lower MSE, which can improve the forecast accuracy and stability of civil aviation passenger volume more effectively, and provide an important reference for the development of air transport production plan and air transport industry.展开更多
文摘本研究应用阅读获得的SVR模型(Si mple View of Reading),探讨单词解码、英语语言理解和一般认知能力在汉语儿童英语阅读学习中的作用及其影响途径。92名小学四年级学生完成了英语阅读理解、英语假词拼读、英语语言理解、汉语语言理解和一般认知能力测验,其中47名学生还完成了英语单词认读测验。相关和回归分析结果表明,控制一般认知能力后,单词解码和英语语言理解均能显著地解释英语阅读理解;进一步控制汉语语言理解后,英语语言理解对英语阅读理解仍具有显著的独立贡献。单词认读和英语语言理解对于英语阅读理解存在显著的交互作用。一般认知能力对英语阅读理解没有显著的直接作用,而以假词拼读能力为中介间接影响英语阅读理解。
文摘为提高民航客运量预测精准度,本文针对近18年的时间序列民航客运量数据,构建极限梯度提升树XGBoost预测模型,进行多特征分析,处理季节、节假日等主要因素,并与SVR模型进行对比。通过对比预测曲线图,反映出SVR模型在高维空间中可以找到最优超平面来拟合数据,XGBoost模型适用于复杂的非线性关系建模。实验结果表明,XGBoost预测模型相比于SVR向量回归模型、线性模型与随机森林模型,其精准度更高且对影响因素敏感;XGBoost模型有更高的R2和更低的MSE,能够更有效提高民航客运量的预测精度和预测稳定性,为制定航空运输生产计划和发展航空运输业提供了重要参考。In order to improve the accuracy of civil aviation passenger traffic prediction, this paper, based on the civil aviation passenger traffic data of recent 18 years, builds the ultimate gradient lift tree XGBoost prediction model, conducts multi-feature analysis, processes major factors such as seasons and holidays, and compares it with the SVR model. By comparing the prediction curves, it shows that SVR model can find the optimal hyperplane to fit the data in the high-dimensional space, and XGBoost model is suitable for complex nonlinear relationship modeling. The experimental results show that compared with SVR vector regression model, linear model and random forest model, XGBoost prediction model is more accurate and sensitive to influencing factors. XGBoost model has higher R2 and lower MSE, which can improve the forecast accuracy and stability of civil aviation passenger volume more effectively, and provide an important reference for the development of air transport production plan and air transport industry.
