一种改进的基于粒子群优化的SVM训练算法

Improved particle swarm optimization for SVM training

支持向量机的训练需要求解一个带约束的二次规划问题,但在数据规模很大的情况下,经典的训练算法将会变得非常困难。提出了一种改进的基于粒子群的优化算法,用于替代支持向量机中现有的训练算法。在改进后的粒子群优化算法中,粒子不仅向自身最优和全局最优学习,还以一定的概率向其他部分粒子的均值学习。同时,还引进了自适应变异算子,以降低未成熟收敛的概率。实验表明,提出的改进训练算法相对改进前的算法在性能上有显著提高。

Since training a SVM requires solving a constrained quadratic programming problem which becomes difficult for very large datasets,an improved particle swarm optimization algorithm is proposed as an alternative to current numeric SVM training methods.In the improved algorithm,the particles studies not only from itself and the best one but also from the mean value of some other particles.In addition,adapiive mutation is introduced to reduce the rate of premature convergence.The experimental results show that the improved algorithm is feasible and effective for SVM training.