An Improved Particle Swarm Optimization for SVM Training

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 was introduced to reduce the rate of premature convergence. The experimental results show that the improved algorithm is feasible and effective for SVM training.

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