论文部分内容阅读
为了避免传统优化方法容易陷入局部最优解的情况,本文采用人工蜂群(Artificial Bee Colony,ABC)方法对支持向量机(Support Vector Machine,SVM)模型的惩罚因子C和宽度参数σ进行参数优化,兼顾了局部最优解和全局最优解,建立了ABC-SVM模型,并将此模型应用在手指静脉图像质量评估中。通过与未经过参数优化的SVM模型对比,同时也与蚁群算法(ACO)、遗传算法(GA)、粒子群算法(PSO)三种优化方法进行对比实验,实验结果表明,ABC-SVM模型无论在分类准确率方面,还是运行时间方面,都是可行的,证明其具有良好的应用价值。
In order to avoid the situation that the traditional optimization method is easy to fall into the local optimal solution, the Artificial Bee Colony (ABC) method is used to optimize the penalty factor C and the width parameter σ of Support Vector Machine (SVM) model , Taking into account the local optimal solution and the global optimal solution, established the ABC-SVM model, and applied this model in finger vein image quality assessment. Compared with the SVM model without parameter optimization, this paper also compared with the three optimization methods of ACO, GA and PSO. The experimental results show that the ABC-SVM model has no effect In the classification accuracy, or run time, are feasible, that it has good application value.