Least Squares Support Vector Machines Based on Support Vector Degrees

  • Lijuan Li
  • Youfeng Li
  • Hongye Su
  • Jian Chu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)


A modified least squares support vector machines (LS-SVM) approach, which treats the training data points differently according to their different degrees of importance, is proposed in this paper. On each data point, a support vector degree is defined and it is associated with the corresponding absolute value of Lagrange multiplier. The experiment of identification of pH neutralization process with polluted measuring data is shown in this paper and the result indicates that the method is effective in identification of nonlinear system. By contrast with the basic LS-SVM, the result also shows the priority of the presented new algorithm.


Support Vector Machine Support Vector Lagrange Multiplier Little Square Support Vector Machine Little Square Support Vector Machine Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lijuan Li
    • 1
    • 2
  • Youfeng Li
    • 1
  • Hongye Su
    • 1
  • Jian Chu
    • 1
  1. 1.National Laboratory of Industrial Control Technology, Institute of Advanced Process ControlZhejiang UniversityHangzhouP.R. China
  2. 2.College of AutomationNanjing University of TechnologyNanjingP.R. China

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