CAJ | 학술논문

针对支持向量机的参数优化缺乏理论支持，而SVM交叉检验法选取又较为费时的情况下，提出了基于人工鱼群算法的支持向量机参数优化选取算法，并以SVM分类预测准确率最大为优化原则，利用人工鱼群算法的较好并行性和较强的全局寻优能力，以实现最优目标并得到SVM的最优参数组合。数值实验结果表明：人工鱼群算法在SVM参数优化选取中具有更快的寻优性能，同时具有较高的分类准确率。该方法具有较好的并行性和较强的全局寻优能力。
침대지지향량궤적삼수우화결핍이론지지，이SVM교차검험법선취우교위비시적정황하，제출료기우인공어군산법적지지향량궤삼수우화선취산법，병이SVM분류예측준학솔최대위우화원칙，이용인공어군산법적교호병행성화교강적전국심우능력，이실현최우목표병득도SVM적최우삼수조합。수치실험결과표명：인공어군산법재SVM삼수우화선취중구유경쾌적심우성능，동시구유교고적분류준학솔。해방법구유교호적병행성화교강적전국심우능력。
As considering that the parameter optimization of support vector machine lacks theory support and the SVM cross-validation method spends lots of time on selecting parameters, the parameter optimization selection method of support vec-tor machine is proposed based on artificial fish-swarm algorithm. This method puts the SVM classification prediction accuracy rate as the optimization principle and uses the better parallelism of artificial fish-swarm algorithm and the stronger global optimi-zation ability to achieve the optimal target and obtain optimal parameter combination of SVM. The results of numerical value experi-ments show that the artificial fish-swarm algorithm has faster performance optimization and higher classification accuracy rate in SVM parameters’optimization selection. This method has the better parallelism and the stronger global optimization ability.