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原空间中最小二乘支持向量机的新算法 被引量：3

New method for least squares support vector machine in the primal
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摘要 为了解决原空间中最小二乘支持向量机的解缺乏稀疏性的缺点,提出了Pruning法、MFCV法和IMFCV法并对BDFS法进行了修改和运用。对一个不含有奇异点的系统而言,Pruning法、BDFS法和MFCV法在一定程度上都能实现原空间中最小二乘支持向量机解的稀疏性。BDFS法无论是训练时间还是预测时间都比Pruning法短;和MFCV法比起来,虽然BDFS法的训练时间短,但比MFCV的预测时间长。对一个含有奇异点的系统而言,Pruning法几乎失去了效用;虽然BDFS和MFCV法的训练时间都比IMFCV法的训练时间短,但IMFCV法能成功抑制奇异点从而缩短预测时间。 In order to solve the shortcoming of lacking sparseness in the solution of least squares support vector machine in the primal, the pruning method, multi-fold cross validation （MFCV） method and improved multi-fold cross validation （IMFCV） method are proposed and the backward deletion feature selection （BDFS） method is modified and applied. These methods all realize the sparseness of the least squares support vector machine to a certain degree in the primal for a system without outliers. The predicted time of BDFS is shorter than the pruning＇s; although the training time of BDFS is shorter than MFCV＇s, its predicted time is longer. In the face of a system with outliers, the pruning almost loses effectiveness, and the training time of BDFS and MFCV are both shorter than IMFCV＇s, but IMFCV can oppress outliers and reduces the predicted time remarkably.
作者 赵永平 孙健国
机构地区 南京航空航天大学能源与动力学院
出处 《系统工程与电子技术》 EI CSCD 北大核心 2009年第1期142-145,237,共5页 Systems Engineering and Electronics
基金 国家自然科学基金资助课题(50576033)
关键词 最小二乘支持向量机 PRUNING BDFS MFCV least squares support vector machine pruning backward deletion feature selection multi-fold cross validation
分类号 TP301 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献16

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二级参考文献14

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共引文献4

同被引文献22

  • 1王玲,薄列峰,刘芳,焦李成.稀疏隐空间支持向量机[J].西安电子科技大学学报,2006,33(6):896-901. 被引量：8
  • 2陈爱军,宋执环,李平.基于矢量基学习的最小二乘支持向量机建模[J].控制理论与应用,2007,24(1):1-5. 被引量：21
  • 3甘良志,孙宗海,孙优贤.稀疏最小二乘支持向量机[J].浙江大学学报（工学版）,2007,41(2):245-248. 被引量：27
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  • 6Pijushl Samui, Tim Lansivaara, Madhav R Bhatt. Least square support vector machine applied to slope reliability analysis [J]. Geotechnical and Geological Engineering, 2013, 31 ( 4 ): 1329-1334.
  • 7Xia Xiaolei, Jiao Weidong, Li Kang, et al. A novel sparse least squares support vector machines [C] //5th International Con- ference on Biomedical Engineering and Informatics, 2012.
  • 8Li Yang, Tang Wanmei, Li Mingyong. A least squares support vector machine sparseness algorithm [J]. Journal of Conver- gence Information Technology, 2012, 7 (13) :240-248.
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引证文献3

二级引证文献12

系统工程与电子技术
2009年 第1期

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