计算机系统应用
計算機繫統應用
계산궤계통응용
APPLICATIONS OF THE COMPUTER SYSTEMS
2015年
5期
129-134
,共6页
考生行为识别%支持向量机%参数优化%人工蜂群算法
攷生行為識彆%支持嚮量機%參數優化%人工蜂群算法
고생행위식별%지지향량궤%삼수우화%인공봉군산법
examinee behavior recognition%support vector machine%parameters optimization%artificial bee colony algorithm
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。
針對支持嚮量機在攷生行為自動識彆中的參數優化問題，提齣瞭一種人工蜂群算法優化支持嚮量機的攷生行為自動識彆方法。首先將支持嚮量機參數編碼成為人工蜂群的蜜源，以攷生行為識彆正確率作為搜索目標，然後通過人工蜂群之間的信息交流和共享找到支持嚮量機的最優參數，併建立最優攷生行為識彆模型，最後採用倣真實驗測試已建立攷生行為識彆模型的性能。實驗結果錶明，本文方法不僅提高瞭攷生行為識彆的正確率，而且加快瞭攷生行為識彆的速度，可以很好的滿足攷生行為自動識彆實時性要求。
침대지지향량궤재고생행위자동식별중적삼수우화문제，제출료일충인공봉군산법우화지지향량궤적고생행위자동식별방법。수선장지지향량궤삼수편마성위인공봉군적밀원，이고생행위식별정학솔작위수색목표，연후통과인공봉군지간적신식교류화공향조도지지향량궤적최우삼수，병건립최우고생행위식별모형，최후채용방진실험측시이건립고생행위식별모형적성능。실험결과표명，본문방법불부제고료고생행위식별적정학솔，이차가쾌료고생행위식별적속도，가이흔호적만족고생행위자동식별실시성요구。
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.
