基于活跃目标点粒子群算法的SVM参数选取  

Parameters Selection of SVM Based on Extended APSO Algorithm

作　　者：李景南[1] 任开春[1] 余佳玲[1] 陈福光 吴钊铭[1] 

机构地区：[1]重庆通信学院,重庆

出　　处：《人工智能与机器人研究》2014年第2期19-24,共6页Artificial Intelligence and Robotics Research

摘　　要：支持向量机是最近才兴起的一种分类工具,它广泛用于控制领域,但是其预测精度受到了其参数选取的影响。使用活跃目标点改进粒子群优化算法,利用活跃目标点粒子群算法搜索支持向量机的最优参数组合。对比仿真实验表明:活跃目标点粒子群算法可以正确支持向量机的参数,能够进行较为准确的分类。Support Vector Machine (SVM), a new mathematic modeling tool, has been widely used in many industry applications. The good generalization ability and estimation accuracy are impacted by parameters selection of SVM. Particle Swarm Optimization is improved by using active target. The active target particle swarm optimization was proposed to search the optimal combination of SVM parameters. Simulations show that active target particle swarm optimization is an effective way to search the SVM parameters and has good performance in classification.

关 键 词：支持向量机 活跃目标点粒子群算法 参数选取 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]