TRVR: A Trimmed Relevance Vector Regression Method

摘要：

A novel trimmed relevance vector regression method named TRVR is proposed to provide robust solution for regression. Firstly the likelihood function is redefined as the trimmed likelihood function over a trimmed subset. Then by maximizing the trimmed likelihood function within the relevance vector machine (RVM) framework, the model weights can be learnt. Simultaneously a re-weighted update strategy is utilized to update the subset iteratively until the optimized subset without outliers is obtained, which can lead to the robustness. Finally the experimental evidence has been gathered to show that this proposed method is very robust and effective

作者: Biao YANG Zengke ZHANG Zhengshun SUN

作者单位: Tsinghua University, 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）