DocumentCode :
2899794
Title :
Study on information fusion algorithm and application based on improved SVM
Author :
Wang, Yanhui ; Zhang, Chenchen ; Luo, Jun
Author_Institution :
State Key Lab. of Rail Traffic Control & Safety, Beijing Jiaotong Univ., Beijing, China
fYear :
2010
fDate :
19-22 Sept. 2010
Firstpage :
1271
Lastpage :
1276
Abstract :
Authors presented the information fusion algorithm based on improved SVM, namely, decision tree - support vector machine algorithm (Decision Tree Method-Support Vector Mechines, DTM-SVM). The algorithm overcame the limitations of the conventional SVM classification which applied only to two-classification problem by a “one to many” pattern, solved multi-classification problem and met a wider range of application requirements. Finally, based on the establishment of a freeway traffic state identification evaluation system, the DTM-SVM model was applied to solve the freeway traffic state recognition. Results show that: the algorithm can identify in a shorter time to reach higher recognition accuracy.
Keywords :
decision trees; pattern classification; road traffic; sensor fusion; support vector machines; traffic engineering computing; decision tree method; freeway traffic state recognition; information fusion algorithm; multiclassification problem; support vector machines; Algorithm design and analysis; Classification algorithms; Indexes; Kernel; Support vector machines; Traffic control; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on
Conference_Location :
Funchal
ISSN :
2153-0009
Print_ISBN :
978-1-4244-7657-2
Type :
conf
DOI :
10.1109/ITSC.2010.5624991
Filename :
5624991
Link To Document :
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