Optimization of SVM Kernels and Application to Down Category Recognition

摘要：

In recent years, the use of support vector machines (SVMs)on various classifications has been increasingly popular.However, the results of classification usually depend on the parameters of the model. These parameters are usually picked by experience, experimental compare, and large-scale search or cross-validation provided by software package. Our scheme to optimize SVM hyper-parameters is to minimize an empirical error estimate using a Quasi-Newton optimization method on the validation set. The method has been used successfully in our down category recognition system.

关键词： Support Vector Machines (SVM) Hyper-parameters Quasi-Newton Optimization Method Empirical Error Estimate

作者: Xiaoyan Yang Hongwei Ge

作者单位: The School of Information Technology, Southern YangTze University Wuxi, Jiangshu, Peoples Republic of China

会议类型: 国际会议

会议名称: 2006 International Symposium on Distributed Computing and Applications to Business,Engineering and Science(2006年国际电子、工程及科学领域的分布式计算应用学术研讨会)

会议地点: 杭州

会议语种:英文

页码: 696-700

在线出版日期: 2006-10-12（万方平台首次上网日期，不代表论文的发表时间）