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Support Vector Machine (SVM), a new mathematic modeling tool, has been widely used in many industry applications. The good generalization ability and estimation accuracy are impacted by parameters selection of SVM. Particle Swarm Optimization is improved by using active target. The active target particle swarm optimization was proposed to search the optimal combination of SVM parameters. Simulations show that active target particle swarm optimization is an effective way to search the SVM parameters and has good performance in classification.