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非线性回归支持向量机的SMO算法改进

赵长春 姜晓爱 金英汉

赵长春, 姜晓爱, 金英汉等 . 非线性回归支持向量机的SMO算法改进[J]. 北京航空航天大学学报, 2014, 40(1): 125-130.
引用本文: 赵长春, 姜晓爱, 金英汉等 . 非线性回归支持向量机的SMO算法改进[J]. 北京航空航天大学学报, 2014, 40(1): 125-130.
Zhao Changchun, Jiang Xiaoai, Jin Yinghanet al. Improved SMO algorithm of nonlinear regression support vector machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(1): 125-130. (in Chinese)
Citation: Zhao Changchun, Jiang Xiaoai, Jin Yinghanet al. Improved SMO algorithm of nonlinear regression support vector machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(1): 125-130. (in Chinese)

非线性回归支持向量机的SMO算法改进

详细信息
  • 中图分类号: TP301.6

Improved SMO algorithm of nonlinear regression support vector machine

  • 摘要: 为了解决非线性数据和非线性函数的回归问题,采用了支持向量机序列最小优化算法.原始序列最小优化(SMO,Sequential Minimal Optimization)算法存在训练速度慢和训练结果不稳定的缺点,为了能加快SMO算法的训练速度和提高训练结果稳定性,通过改进优化乘子更新方法、采用双阈值法、预存核函数、增加停机准则等方法对SMO算法做了改进.仿真实验表明,改进的算法能很好地对非线性数据和非线性函数进行回归,具有比原始SMO算法更快的训练速度和稳定的训练结果.

     

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    [9] 王书舟, 伞冶, 张允昌.基于支持向量机改进 SMO 算法的直升机旋翼自转着陆过程建模[J].航空学报, 2009, 30(1): 46-51 Wang Shuzhou, San Ye, Zhang Yunchang.Modeling for landing process of helicopter with rotator self-rotating based on modified SMO algorithm of support vector machine[J].Acta Aeronautica et Astronautica Sinica, 2009, 30(1):46-51(in Chinese)
    [10] 王定成.支持向量机建模预测与控制[M].北京:气象出版社, 2009:48-49 Wang Dingcheng.Prediction and control based on support vector machine modelling[M].Beijing:Meteorological Press, 2009:48-49(in Chinese)
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出版历程
  • 收稿日期:  2013-01-28
  • 网络出版日期:  2014-01-20

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