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Support vector machine (SVM) is a novel and popular technique for pattern classification and regression estimation. In the training process of SVM it is of great importance to determine a few tuning parameters to ensure the good performance of SVM. However, the widely used optimization methods such as Particle Swarm Optimization and Genetic Algorithm have the disadvantages of low convergent speed...
SVM performance is very sensitive to the parameter set. In this paper we propose an automatic and effective model selection method. It is based on evolutionary computation algorithms and use recall, precision and error rate estimated by xialpha-estimate as the optimization targets. Optimized by genetic algorithm (GA) or particle swarm optimization (PSO) algorithm, we demonstrate that SVM could automatically...
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