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《Journal of Chengde Petroleum College》 2012-01
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Application of Improved Support Vector Machine Algorithm in Short-term Traffic Flow Forecast

TANG Shi-xing(Personnel Department,Chengde Petroleum College,Chengde 067000,Hebei,China)  
The paper introduces cross-validation and grid-search method to optimize the prediction accuracy of Support Vector Machine models,the establishment of an improved Support Vector Machine prediction model,and applied to short-term traffic flow forecasting empirical analysis.The paper also uses the real time data of certain urban road to test the efficiency of the proposed model and the result is satisfactory.
【Fund】: 河北省高等学校科学技术研究指导项目:Z2010210
【CateGory Index】: U491.14
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【Citations】
Chinese Journal Full-text Database 6 Hits
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【Co-citations】
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2 YANG Fang-ming~1,ZHU Shun-ying~2(1.Department of Computer Science & Technique,Chongqing Jiaotong University,Chongqing 400074,China;2.Wuhan University of Technology,Hubei Wuhan 430063,China);Short-term traffic flow forecasting based wavelet[J];Journal of Chongqing Jiaotong University;2006-03
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【Secondary Citations】
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