CAJ | 학술논문

支持向量机参数的选择决定着支持向量机的分类精度和泛化能力，而其参数优化缺乏理论指导，在此背景下提出了ACO-SVM模型。该模型将SVM分类预测准确率作为目标函数，对蚁群算法进行改进，引入有向搜索和基于时变函数更新的信息素更新原则，利用蚁群算法的并行性、正反馈机制和较强的鲁棒性，以求得最优目标并得到SVM的最优参数组合。数值实验结果表明，改进蚁群算法在SVM参数优化选取中具有更好的寻优性能，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。
지지향량궤삼수적선택결정착지지향량궤적분류정도화범화능력，이기삼수우화결핍이론지도，재차배경하제출료ACO-SVM모형。해모형장SVM분류예측준학솔작위목표함수，대의군산법진행개진，인입유향수색화기우시변함수경신적신식소경신원칙，이용의군산법적병행성、정반궤궤제화교강적로봉성，이구득최우목표병득도SVM적최우삼수조합。수치실험결과표명，개진의군산법재SVM삼수우화선취중구유경호적심우성능，구유교고적분류준학솔；해방법구유교호적병행성화교강적전국심우능력。
SVM parameter selection determines SVM classification accuracy and generalization ability, and its lack of theoretical guidance parameter optimization, ACO-SVM model is proposed, it predicts the SVM classification accuracy as the objective function, and improves the ant colony algorithm, with the introduction of search and updates the pheromone based on time-varying function update policy, uses the ant colony algorithm parallelism, positive feedback mechanism and strong robustness, in order to achieve optimal goals and get the optimal combination of parameters of SVM. The results of numerical value experiments show that the improved Ant Colony Optimization algorithm for SVM parameters selection has better optimization performance and higher classification accuracy. This method has the better parallelism and strong global optimization ability.