A Method for Improving SVM Classifier by Excluding Redundant Information

摘要：

This paper proposes that Support Vectors include redundant information after analyzing kernels geometrical structure and researching data dependant method for improving Support Vector Machine (SVM). Redundant information confuses the law of a learning problem. Data dependant method on improving SVM is based on Riemannian geometry theory and could exclude redundant information. Reasoning and experiments show this method could effectively improve classification ability and classification speed of SVM.

作者: Bing PENG Jianzhong ZHOU Fang LIU Rengcun FANG

作者单位: Huazhong University of Science and Technology, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）