摘要
针对支持向量机在考生行为自动识别中的参数优化问题,提出了一种人工蜂群算法优化支持向量机的考生行为自动识别方法.首先将支持向量机参数编码成为人工蜂群的蜜源,以考生行为识别正确率作为搜索目标,然后通过人工蜂群之间的信息交流和共享找到支持向量机的最优参数,并建立最优考生行为识别模型,最后采用仿真实验测试已建立考生行为识别模型的性能.实验结果表明,本文方法不仅提高了考生行为识别的正确率,而且加快了考生行为识别的速度,可以很好的满足考生行为自动识别实时性要求.
According to the parameter optimization of support vector machine in the examinee behavior automatic recognition, an examinee behavior automatic recognition method based on artificial bee colony algorithm optimized parameters of support vector machine is proposed in this paper. Firstly, the parameters of support vector machine are encoded into artificial bee colony nectar and examinee behavior recognition correct rate is taken as searching target, and then the parameters of support vector machine is selected by exchange and sharing of information of artificial bee colony to establish the optimal examinee behavior recognition model, finally the performance is tested by simulation experiments. The experimental results show that, the proposed method not only improves the recognition correct rate of the examinee behavior, but also accelerate recognition speed, so it can meet the real-time requirements of examinee behavior recognition.
出处
《计算机系统应用》
2015年第5期129-134,共6页
Computer Systems & Applications
关键词
考生行为识别
支持向量机
参数优化
人工蜂群算法
examinee behavior recognition
support vector machine
parameters optimization
artificial bee colonyalgorithm
