自适应误差惩罚支撑向量回归机

陈晓峰; 王士同; 曹苏群

doi:10.3724/SP.J.1146.2006.01081

自适应误差惩罚支撑向量回归机

doi: 10.3724/SP.J.1146.2006.01081

陈晓峰^{①;王士同},
王士同,
曹苏群

基金项目:

2004年教育部优秀人才支持计划(NCET-04-0496)，模式识别国家重点实验室开放课题，南京大学软件新技术国家重点实验室开放课题，教育部重点科学研究项目(105087)和国防应用基础研究基金项目(A1420061266)资助课题

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出版历程
- 收稿日期: 2006-07-20
- 修回日期: 2007-01-31
- 刊出日期: 2008-02-19

SVR with Adaptive Error Penalization

摘要

摘要: 该文提出一种支撑向量回归机AEPSVR。它首先用 -SVR求得一个近似的支撑向量回归函数，在此基础上，引入一种新自适应误差惩罚函数，通过迭代，得到鲁棒的支撑向量回归机。该方法因以 -SVR为基础，故可以应用各种求解SVR的优化算法。实验表明，该支撑向量回归机AEPSVR能显著地降低离群点的影响，具有良好的泛化性能。
- 支撑向量回归; 离群点; 自适应误差惩罚
Abstract: A novel support vector regression method AEPSVR is proposed in this paper. First, an approximate regression function is obtained using -SVR method, and then a new adaptive error penalization function is introduced to enhance the robust performance of SVR such that a robust support vector regression is derived. Because the proposed AEPSVR here is based on -SVR, so various optimization methods for SVR can be used. Experimental results show that the proposed AEPSVR can reduce the affect of outliers, and have the very good generalization capability.

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