基于动量粒子群的混合核SVM参数优化方法

支持向量机（SVM）可以很好地用来解决分类问题，参数优化尤其重要。混合核函数的引入，使得SVM又多了一个可调参数。针对该参数用人工或经验的方法获取具有局限性，采用动量粒子群（MPSO）对SVM基本参数、混合可调核参数进行综合寻优，来寻找最佳参数组合。通过UCI数据仿真，对比结果表明：所提优化方法能够快速有效地提取最佳参数组合，所得SVM性能明显提高，分类效果更好。

Parameter optimization of mixed kernel SVM based on momentum particle swarm optimization

Support Vector Machine (SVM) can be used to solve classification problems, and it is very important to optimize its parameters. With the introduction of mixed kernels, SVM has one more adjustable parameter. Because it is hard to obtain the parameter by manual or experience, Momentum Particle Swarm Optimization (MPSO) was used to find the best combination of the basic parameters and mixed adjustable nuclear parameter of SVM. Finally, the simulations of UCI data show that the proposed algorithm provides an effective way to search the best parameters combination, and makes SVM have higher performance and better classification accuracy.