Automation and Remote Control

, Volume 78, Issue 1, pp 138–145 | Cite as

The problem of choosing the kernel for one-class support vector machines

  • A. N. BudynkovEmail author
  • S. I. Masolkin
Control Sciences


The article presents a review of one-class support vector machine (1-SVM) used when there is not enough data for abnormal technological object’s behavior detection. Investigated are three procedures of the SVM’s kernel parameter evaluation. Two of them are known in literature as the cross validation method and the maximum dispersion method, and the third one is an author-suggested modification of the maximum dispersion method, minimizing the kernel matrix’s entropy. It is shown that for classification without counting training data set ejections the suggested procedure provides the classification’s quality equal to the first one, and with less value of the kernel parameter.


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Copyright information

© Pleiades Publishing, Ltd. 2017

Authors and Affiliations

  1. 1.Trapeznikov Institute of Control SciencesRussian Academy of SciencesMoscowRussia

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