Training Support Vector Machines with Quantum-behaved Particle Swarms Optimization

摘要：

Large number of example vectors brings difficulties for quadratic programmin g problem with support vector machines, traditional methods may be impossible.Quantum-behaved Particle Swarm Optimiz ation presented by the author is a new method of optimization.It is better than classical Particle Swarm Optimiza- tion(PSO for short) in convergence and stability of the overall.Testify QPSO has determinate applied value in the field of support vector machines,and it is a new way for quadratic programming problem with a large number of example vectors.

关键词： SVM QPSO Classification MNIST

作者: Hui Li Wenbo Xu Jun Sun

作者单位: College of Information,Jiangnan University,Wuxi,214122

会议类型: 国际会议

会议名称: 2008年国际电子商务、工程及科学领域的分布式计算和应用学术研讨会(2008 International Symposium on Distributed Computing and Applications for Business Engineering and Science)

会议地点: 大连

会议语种:英文

页码: 603-608

在线出版日期: 2008-07-27（万方平台首次上网日期，不代表论文的发表时间）