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基于模糊多核学习的改进支持向量机算法研究 被引量：4

Study on Improved SVM Algorithm Based on Fuzzy Multiple Kernel Learning
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摘要 针对传统SVM对噪声点和孤立点敏感的问题,以及不能解决样本特征规模大、含有异构信息、在特征空间中分布不平坦的问题,将模糊隶属度融入多核学习中,提出了一种模糊多核学习的方法;通过实验验证了模糊多核学习比传统SVM、模糊支持向量机以及多核学习具有更好的分类效果,从而验证了所提方法能够有效的克服传统SVM对噪声点敏感以及数据分布不平坦的问题。 According to issues on noise and isolated points sensitive,the traditional SVM can not solve the sample characteristics of large scale,containing the heterogeneous information and the uneven feature space distribution.The fuzzy membership is draw into multiple kernel learning,and this paper proposes a method of fuzzy multiple kernel learning.Experimental results show that fuzzy multiple kernel learning is more effective and feasible than SVM,Fuzzy support vector machine and Multiple kernel learning.So,proposed method in this paper can effectively overcome the traditional SVM on noise sensitive and data distribution uneven problem.
作者 刘建峰 淦燕
机构地区 重庆三峡学院教务处 电子科技大学计算机视听觉实验室
出处 《计算机测量与控制》 2016年第3期231-233,共3页 Computer Measurement &Control
基金 重庆市自然科学项目(cstc2014jcyjA40011) 重庆市教委科技项目(KJ1400513)
关键词 支持向量机 模糊支持向量机 多核学习 模糊多核学习 support vector machine fuzzy support vector machine multiple kernel learning fuzzy multiple kernel learning
分类号 TP18 [自动化与计算机技术—控制理论与控制工程] TP391.4 [自动化与计算机技术—计算机应用技术]
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参考文献8

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二级参考文献20

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共引文献5

同被引文献41

  • 1谢磊,王树青.递归核PCA及其在非线性过程自适应监控中的应用[J].化工学报,2007,58(7):1776-1782. 被引量：12
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  • 3Choi S W, Lee C, Lee J M, et al. Fault detection and identifica- tion of nonlinear processes based on kernel PCA [J].Chemometrics and Intelligent Laboratory Systems, 2005, 75 (1) : 55 - 67.
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  • 8Xiao Yingchao, Wang Huangang, Xu Wenli. Model selection of Gaussian kernel PCA for novelty detection [J]. Chemometrics and Intelligent Laboratory Systems, 2014, 136 : 164 - 172.
  • 9Jia Mingxing, Xu Hengyuan, Liu Xiaofei, et al. The optimization of the kind and parameters of kernel function in KPCA for process monitoring [J]. Computers and Chemical Engineering , 2012, 46: 94-104.
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引证文献4

二级引证文献20

计算机测量与控制
2016年 第3期

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