Incremental and Decremental LS-SVM for Function Estimation

摘要：

This article proposes an incremental and decremental LS-SVM (Least Square Support Vector Machines) for function estimation, which can make use of the current information phis the new data in an incremental formation, and prune the LS-SVM to get sparse approximation online. When a SV is added or removed, the incremental and decremental formation avoids large-scale matrix inversion operation. Thus the computation cost is reduced and the online training becomes possible. The experiments of function estimation on the artificial and real data have shown the feasibility.

作者: Liu Jianghua Cheng Junshi Chen Jiapin

作者单位: Information Storage Research Center Shanghai Jiaotong University, Shanghai 200030

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 1410-1413

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）