基于QPSO算法的支持向量机参数优化研究

应用具有量子行为的粒子群优化算法，对支持向量（SVM）进行参数优化研究．根据支持向量机的分类准确率和泛化能力之间的关系，应用QPSO算法选取比较优秀的参数模型，比较参数模型的各项性能，选取最适合实际需要的参数模型．仿真表明，QPSO算法的SVM模型与PSO算法相比在分类准确率和泛化能力上均获得更好的效果，经QPSO优化后的SVM整体性能明显提高．

Research on Support Vector Machine with Optimized Parameters Based on Quantum-behaved Particle Swarm Optimization Algorithm

Quantum-behaved Particle Swarm Optimization （QPSO） is utilized to research parame- ter optimization of Support Vector Machine （SVM）. According to the relationship between classi- fication accuracy and generalization of SVM,better parameter models are chosen to compare their performances in order to obtain the parameter model which is the most suitable to the actual re- quirement. Simulation shows that QPSO can obtain the better parameter model in classification accuracy and generalization and the over-all performance of SVM has a great improvement after it has been optimized by QPSO.