摘要
支持向量机(Support Vector Machine,SVM)是建立在统计学习理论和结构风险最小化准则基础上的机器学习方法,该方法可以较好的解决以往很多学习方法的小样本、高维数、非线性和局部最小点等实际问题。本文利用支持向量机(SVM)回归理论和方法,建立基于核函数主成分支持向量机(Kernel Principal Component Analysis-Support Vector Machine,KP-CA-SVM)回归模型,并用2000-2008年杭州市公路客运量为样本进行了预测,结果表明,KPCA-SVM模型具有较高的预测精度和可靠性,是一种有效的公路客运量预测方法。
Support vector machine is a machine learning method based on statistical learning theory and structural risk minimization.Support vector machine is much better method than ever,because the method may solve some actual problem in the small samples,high dimension,nonlinear and local minima and so on.The article utilizes the theory and method of support vector machine（SVM）regression,and establishes the regressive model based on Kernel Principal Component Analysis-Support Vector Machine（KPCA-SVM）.Through forecasting the passenger traffic of Hangzhou＇s highway in 2000-2008,we show that the regressive model of KPCA-SVM has much higher accuracy and reliability of prediction,and may effectively forecast the passenger traffic of highway.
出处
《技术经济与管理研究》
2012年第1期8-12,共5页
Journal of Technical Economics & Management
基金
国家自然科学基金项目(项目编号:60979016)
教育部新世纪优秀人才支持项目资助(NCET-08-0171)
高等学校博士点专项基金资助项目(20092302110060)
关键词
KPCA
公路客运
预测研究
运量预测
KPCA
Highway passenger
Prediction research
Traffic volume forecast
