关键词: 支持向量机, 粒子群优化算法, 自适应变异

Abstract: 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，adaptive 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.

Key words: Support Vector Machine（SVM）, particle swarm optimization algorithm, adaptive mutation