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  1. (西安电子科技大学 理学院,陕西 西安 710071)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2007-10-20 发布日期:2007-10-25

Fuzzy least square support vector machines for regression

WU Qing;LIU San-yang;DU Zhe

  1. (School of Science, Xidian Univ., Xi′an 710071, China)
  • Received:1900-01-01 Revised:1900-01-01 Online:2007-10-20 Published:2007-10-25

摘要: 为了克服最小二乘支持向量机对于孤立点过分敏感的问题,将模糊隶属度概念引入最小二乘支持向量机中,提出了基于支持向量域描述的模糊最小二乘支持向量回归机.该方法先对样本进行数据域描述得到一个包含该组数据的最小半径的超球,再根据特征空间中样本与超球球心的距离确定它们的隶属度,减少了奇异点(噪声)的影响;把所要求解的约束凸二次优化问题转化为正定线性方程组,并采用快速Cholesky分解的方法求解该方程组.实验结果表明该方法在不牺牲训练速度的前提下,比支持向量机和最小二乘支持向量机具有更高的预测精度.

关键词: 最小二乘支持向量机, 模糊隶属度, 数据域描述

Abstract: The conception of fuzzy membership is introduced into least square support vector machines(LSSVMs), which overcomes the disadvantage that LSSVMs are so sensitive to outliers in training samples. And then fuzzy least square support vector machines (FLSSVMs) are proposed based on support vector domain description (SVDD). Data samples in the feature space are described and the smallest enclosing hypersphere is obtained. The fuzzy membership value to each sample point is determined according to the distance of each sample from the center of the hypersphere, which can reduce the effect of outliers. Numerical results show that the predictive precision of the proposed method is higher than that of SVMs and LSSVMs without decreasing the training speed.

Key words: least square support vector machines, fuzzy membership, data domain description


  • TP18