Support vector machine (SVM) parameters such as penalty parameter and kernel parameters have a great influence on the complexity and accuracy of SVM model. In this paper, quantum-behaved particle swarm optimization (QPSO) has been employed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (QPSO-SVM), the experiment adopted seven standard classification datasets which are obtained from UCI machine learning data repository. For verification, the results of the QPSO-SVM algorithm are compared with the standard PSO, and genetic algorithm (GA) which is one of the well-known optimization algorithms. Moreover, the results of QPSO are compared with the grid search, which is a conventional method of searching parameter values. The experimental results demonstrated that the proposed model is capable to find the optimal values of the SVM parameters. The results also showed lower classification error rates compared with standard PSO and GA algorithms.

中文翻译：

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支持向量机参数优化的量子行为粒子群优化

惩罚参数、核参数等支持向量机(SVM)参数对SVM模型的复杂度和准确度影响很大。本文采用量子行为粒子群优化(QPSO)对SVM的参数进行优化，以减少分类误差。为了评估所提出的模型（QPSO-SVM），实验采用了从 UCI 机器学习数据库中获得的七个标准分类数据集。为了验证，将QPSO-SVM算法的结果与标准PSO和众所周知的优化算法之一的遗传算法（GA）进行比较。此外，QPSO 的结果与网格搜索进行了比较，网格搜索是搜索参数值的传统方法。实验结果表明，所提出的模型能够找到SVM参数的最佳值。结果还表明，与标准 PSO 和 GA 算法相比，分类错误率更低。