基于改进PSO算法的混合核SVM算法

摘要

We proposed a hybrid kernel support vector machine (SVM ) algorithm based on improved particle swarm optimization (PSO ) algorithm ,which solved the problem that the general hybrid kernel SVM algorithm was difficult to evaluate the parameter selection .The algorithm improved the convergence property by limiting the velocity of the particle , the search space and the crossover operator to get the best combination of the parameters . Simulation experiments show that the algorithm can get the optimal value of parameters more quickly and effectively .%提出一种基于改进粒子群优化(PS O)算法的优化混合核支持向量机(S V M)算法(ILPSO),解决了一般混合核SVM算法很难评定参数选择的问题.该算法通过限定粒子的速度、搜索空间和交叉算子等多种寻优策略加强其收敛特性,得到了参数的最佳组合.仿真实验表明,该算法能更快速、有效地获得参数的最优值.