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To selectparameters of support vector machine (SVM) during application, we choose sample sets from UCI database. TheparametersoptimizationofSVM with kernel function is achieved throughtraditional grid method, particle swarm optimization algorithmand genetic algorithm inintelligentoptimization algorithms. And the optimum parameters are applied to testingsamples. The kernel function optimization strategy is obtained after athorough analysis of the relationship between parameters, the influence of parameters on the performance of SVMand the pros and cons of both traditional and intelligent optimization algorithms. The users should use intelligent optimization algorithm preliminarily to make sure the scope ofparameters, and couple it with grid method to obtain a high degree of accuracy.Experimental datas verify the effectiveness of the strategy, which expandsthe generalization and application ofSVM. 