APPLICATION OF THE IMPROVED SUPPORT VECTOR MACHINE ON VEHICLE RECOGNITION
Due to factors of affecting vehicle recognition is many and complex, the affecting degree of every factor is different, and the borderline is fuzzy, so it is difficult to estimate together using traditional mathematics model. The support vector machine (SVM) is a new machine study method. In this paper, a vehicle recognition model based on Least Squares Support Vector Machine is presented. In the model, the quadratic programming problem is simplified as the problem of solving linear equation groups, and the SVM algorithm is realized by least squares method. It is presented to choose parameter of kernel function by dynamic way, which enhances preciseness rate of recognition. The simulation results show the model has strong non-linear solution and anti-jamming ability, and can enhances preciseness rate of recognition.
Vehicle recognition Least squares support vector machine Kernel function
KUI-HE YANG LING-LING ZHAO
College of Information, Hebei University of Science and Technology, Shijiazhuang 050018, China
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
2785-2789
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）