| 引用本文: | 周晓剑,马义中,朱嘉钢,刘利平,汪建均.求解非半正定核Huber–支持向量回归机问题的序列最小最优化算法[J].控制理论与应用,2010,27(9):1178~1184.[点击复制] |
| ZHOU Xiao-jian,MA Yi-zhong,ZHU Jia-gang,LIU Li-ping,WANG Jian-jun.Sequential-minimal-optimization algorithm for solving Huber-suppor-vector-regression with non-positive semi-definite kernels[J].Control Theory and Technology,2010,27(9):1178~1184.[点击复制] |
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| 求解非半正定核Huber–支持向量回归机问题的序列最小最优化算法 |
| Sequential-minimal-optimization algorithm for solving Huber-suppor-vector-regression with non-positive semi-definite kernels |
| 摘要点击 2211 全文点击 1762 投稿时间：2009-04-08 修订日期：2009-10-30 |
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| DOI编号 10.7641/j.issn.1000-8152.2010.9.CCTA090393 |
| 2010,27(9):1178-1184 |
| 中文关键词 支持向量机 非半正定核 序列最小最优化算法 Huber–支持向量回归机 |
| 英文关键词 support-vector-machine non-positive semi-definite kernel sequential-minimal-optimization algorithm Huber-support vector regression |
| 基金项目 国家自然科学基金重点资助项目(70931002); 国家自然科学基金资助项目(70672088). |
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| 中文摘要 |
| 序列最小最优化(SMO)算法是求解大型支持向量机(SVM)问题的有效算法. 已有的算法都要求核函数是正定的或半正定的, 从而使其应用受到限制. 针对这种缺点, 本文提出一种新的的SMO算法,可求解非半正定核Huber-SVR问题. 提出的算法在保证收敛的前提下可使非半正定Huber-SVR能够达到比较理想的回归精度, 因而具有一定的理论意义和实用价值. |
| 英文摘要 |
| Sequential-minimal-optimization(SMO) algorithm is effective in solving large-scale support-vectormachine(SVM) problems. However, the existing algorithms require the kernel functions to be positive definite(PD) or positive semi-definite(PSD), thus limiting their applications. Having considered their deficiencies, we propose a new algorithm for solving Huber-SVR problems with non-positive semi-definite(non-PSD) kernels. This algorithm provides desirable regression-accuracies while ensuring the convergence. Thus, it is of theoretical and practical significance. |