Study on Traffic Information Fusion Algorithm Based on Support Vector Machines
Support vector machine (SVM) is a new sort of machine learning method based on structure risk minimization (SRM) principle, which has high generalization capability. Many problems with small samples, nonlinearity or high dimension in pattern recognition could be solved by the method. In this paper,...
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| creator | Haihong Liu Xiaoyuan Wang Derong Tan Lei Wang |
| description | Support vector machine (SVM) is a new sort of machine learning method based on structure risk minimization (SRM) principle, which has high generalization capability. Many problems with small samples, nonlinearity or high dimension in pattern recognition could be solved by the method. In this paper, the traffic data on freeway were taken as research objects and an information fusion algorithm based on SVM about freeway incident detection was proposed. A SVM was trained and tested using the data obtained from the simulation under the condition of incident and non-incident. Compared with the multi-layer feed forward neural network (MLF) algorithm trained with the same data, the simulation results showed that the SVM offers a lower misclassification rate, higher correct detection rate and lower false alarm, and it can improve the detection performance |
| doi_str_mv | 10.1109/ISDA.2006.259 |
| format | Conference Proceeding |
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| ispartof | Sixth International Conference on Intelligent Systems Design and Applications, 2006, Vol.1, p.183-187 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Feeds Learning systems Machine learning algorithms Object detection Pattern recognition Risk management Support vector machines Telecommunication traffic Testing Traffic control |
| title | Study on Traffic Information Fusion Algorithm Based on Support Vector Machines |
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