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| Accuracy Analysis of SVM Based Ballistic Recognition Approach |
| TAO Qing1,2, NA Jian1, FENG Yong1, LIU Xin1 |
1.New Star Research Institute of Applied Technology, Hefei 230031
2.The Key Laboratory of Complex System and Intelligence Science, Institute of Automation,Chinese Academy of Sciences, Beijing 100080
3.Robot Sensor Laboratory, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031 |
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Abstract The ballistic recognition accuracy is further discussed and analyzed in terms of simulation experiments. Firstly, the reason for causing the misclassified samples is analyzed. Then, the influences of the number of training samples, the interval of sampling and the noise of radar on the recognition accuracy are respectively discussed in detail. Finally, several significant and interesting results are achieved.
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Received: 25 June 2008
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[7] Tao Qing, Wu Gaowei, Wang Feiyue, et al. Posterior Probability Support Vector Machines for Unbalanced Data. IEEE Trans on Neural Networks, 2005, 16(6): 1561- 1573
[8] Tao Qing, Wu Gaowei, Wang Jue. A General Soft Method for Learning SVM Classifiers with L1-Norm Penalty. Pattern Recognition, 2008, 41(3): 939-948
[9] Tao Qing, Chu Dejun, Wang Jue. Recursive Support Vector Machines for Dimensionality Reduction. IEEE Trans on Neural Networks, 2008, 19(1): 189-193 |
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2009, Vol. 22 
