Experimental analysis of Support Vector Machines with different kernels based on non-intrusive monitoring data

Takashi Onoda, Hiroshi Murata, Gunnar Rätsch, Klaus Robert Müller

Research output: Contribution to conferencePaper

15 Citations (Scopus)

Abstract

The estimation of the states of household electric appliances has served as the first application of Support Vector Machines in the power system research field [10]. Thus, it is imperative for power system research field to evaluate the Support Vector Machine on this task from a practical point of view. In this paper, we use the data proposed in [10] for this purpose. We put particular emphasis on comparing different types of Support Vector Machines obtained by choosing different kernels. We report results for polynomial kernels, radial basis function kernels, and sigmoid kernels. In handwritten digit recognition research, all results for the three different kernels achieved almost same error rates. However, in the estimation of the states of household electric appliances, the results for the three different kernels achieved different error rates. We also put particular emphasis on comparing different capacity of Support Vector Machines obtained by choosing different regularization constants and parameters of kernels. The results show that the choice of regularization constants and parameters of kernels is as important as the choice of kernel functions for real world applications.

Original languageEnglish
Pages2186-2191
Number of pages6
Publication statusPublished - 2002
Event2002 International Joint Conference on Neural Networks (IJCNN'02) - Honolulu, HI, United States
Duration: 2002 May 122002 May 17

Other

Other2002 International Joint Conference on Neural Networks (IJCNN'02)
CountryUnited States
CityHonolulu, HI
Period02/5/1202/5/17

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Onoda, T., Murata, H., Rätsch, G., & Müller, K. R. (2002). Experimental analysis of Support Vector Machines with different kernels based on non-intrusive monitoring data. 2186-2191. Paper presented at 2002 International Joint Conference on Neural Networks (IJCNN'02), Honolulu, HI, United States.